Journal articles: 'Recommendation algorithms'

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Shang, Songtao, Wenqian Shang, Minyong Shi, Shuchao Feng, and Zhiguo Hong. "A Video Recommendation Algorithm Based on Hyperlink-Graph Model." International Journal of Software Innovation 5, no. 3 (July 2017): 49–63. http://dx.doi.org/10.4018/ijsi.2017070104.

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The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.

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Adomavicius, Gediminas, and Jingjing Zhang. "Stability of Recommendation Algorithms." ACM Transactions on Information Systems 30, no. 4 (November 2012): 1–31. http://dx.doi.org/10.1145/2382438.2382442.

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Kaiser, Jonas, and Adrian Rauchfleisch. "Birds of a Feather Get Recommended Together: Algorithmic Homophily in YouTube’s Channel Recommendations in the United States and Germany." Social Media + Society 6, no. 4 (October 2020): 205630512096991. http://dx.doi.org/10.1177/2056305120969914.

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Algorithms and especially recommendation algorithms play an important role online, most notably on YouTube. Yet, little is known about the network communities that these algorithms form. We analyzed the channel recommendations on YouTube to map the communities that the social network is creating through its algorithms and to test the network for homophily, that is, the connectedness between communities. We find that YouTube’s channel recommendation algorithm fosters the creation of highly homophilous communities in the United States ( n = 13,529 channels) and in Germany ( n = 8,000 channels). Factors that seem to drive YouTube’s recommendations are topics, language, and location. We highlight the issue of homophilous communities in the context of politics where YouTube’s algorithms create far-right communities in both countries.

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Jalili, Mahdi. "A Survey of Collaborative Filtering Recommender Algorithms and Their Evaluation Metrics." International Journal of System Modeling and Simulation 2, no. 2 (June 30, 2017): 14. http://dx.doi.org/10.24178/ijsms.2017.2.2.14.

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Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items), and then building the recommendation lists. There is no unique evaluation metric to assess the performance of recommendations systems, and one often choose the one most appropriate for the application in hand. This paper compares the performance of a number of well-known collaborative filtering algorithms on movie recommendation. To this end, a number of performance criteria are used to test the algorithms. The algorithms are ranked for each evaluation metric and a rank aggregation method is used to determine the wining algorithm. Our experiments show that the probabilistic matrix factorization has the top performance in this dataset, followed by item-based and user-based collaborative filtering. Non-negative matrix factorization and Slope 1 has the worst performance among the considered algorithms. Keywords—Social networks analysis and mining, big data, recommender systems, collaborative filtering.

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Cai, Biao, Xiaowang Yang, Yusheng Huang, Hongjun Li, and Qiang Sang. "A Triangular Personalized Recommendation Algorithm for Improving Diversity." Discrete Dynamics in Nature and Society 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3162068.

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Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

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Li, Xiaofeng, and Dong Li. "An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy." Mobile Information Systems 2019 (May 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/3560968.

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The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

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Li, Jing, and Zhou Ye. "Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm." Complexity 2020 (December 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/6619249.

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In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online.

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Bin, Sheng, and Gengxin Sun. "Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships." Mathematical Problems in Engineering 2021 (February 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/6610645.

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With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

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Karwowski, Waldemar, Marian Rusek, and Joanna Sosnowska. "THE RECOMMENDATION ALGORITHM FOR AN ONLINE ART GALLERY." Information System in Management 7, no. 2 (June 30, 2018): 108–19. http://dx.doi.org/10.22630/isim.2018.7.2.10.

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The paper discusses the need for recommendations and the basic recommendation systems and algorithms. In the second part the design and implementation of the recommender system for online art gallery (photos, drawings, and paintings) is presented. The designed customized recommendation algorithm is based on collaborative filtering technique using the similarity between objects, improved by information from user profile. At the end conclusions of performed algorithm are formulated.

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Zhao, Ji-chun, Shi-hong Liu, and Jun-feng Zhang. "Personalized Distance Learning System based on Sequence Analysis Algorithm." International Journal of Online Engineering (iJOE) 11, no. 7 (August 31, 2015): 33. http://dx.doi.org/10.3991/ijoe.v11i7.4764.

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Personalized learning system can provide users with the most valuable learning resource to them through intelligent recommendation models and algorithms. This paper proposed the classical sequence analysis algorithms, and the Prefixspan algorithm is validated through distance learning platform data. In the event that the minimum support threshold is between 0.003 to 0.004%, test data shows that the performance of the algorithm's accuracy rate is relatively stable and the recommendation effect is satisfactory.

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Wang, G., Q. Song, H. Sun, X. Zhang, B. Xu, and Y. Zhou. "A Feature Subset Selection Algorithm Automatic Recommendation Method." Journal of Artificial Intelligence Research 47 (May 15, 2013): 1–34. http://dx.doi.org/10.1613/jair.3831.

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Many feature subset selection (FSS) algorithms have been proposed, but not all of them are appropriate for a given feature selection problem. At the same time, so far there is rarely a good way to choose appropriate FSS algorithms for the problem at hand. Thus, FSS algorithm automatic recommendation is very important and practically useful. In this paper, a meta learning based FSS algorithm automatic recommendation method is presented. The proposed method first identifies the data sets that are most similar to the one at hand by the k-nearest neighbor classification algorithm, and the distances among these data sets are calculated based on the commonly-used data set characteristics. Then, it ranks all the candidate FSS algorithms according to their performance on these similar data sets, and chooses the algorithms with best performance as the appropriate ones. The performance of the candidate FSS algorithms is evaluated by a multi-criteria metric that takes into account not only the classification accuracy over the selected features, but also the runtime of feature selection and the number of selected features. The proposed recommendation method is extensively tested on 115 real world data sets with 22 well-known and frequently-used different FSS algorithms for five representative classifiers. The results show the effectiveness of our proposed FSS algorithm recommendation method.

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Hu, Jinyu, Zhiwei Gao, and Weisen Pan. "Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/248084.

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Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.

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ping, Hua Quan, and Xiang Ming. "Research on Several Recommendation Algorithms." Procedia Engineering 29 (2012): 2427–31. http://dx.doi.org/10.1016/j.proeng.2012.01.326.

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周, 春华. "Review of Classical Recommendation Algorithms." Computer Science and Application 09, no. 09 (2019): 1803–13. http://dx.doi.org/10.12677/csa.2019.99202.

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V. Tatiya, Ruchita. "A Survey of Recommendation Algorithms." IOSR Journal of Computer Engineering 16, no. 6 (2014): 16–19. http://dx.doi.org/10.9790/0661-16651619.

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De Pessemier, Toon, Simon Dooms, and Luc Martens. "Comparison of group recommendation algorithms." Multimedia Tools and Applications 72, no. 3 (June 30, 2013): 2497–541. http://dx.doi.org/10.1007/s11042-013-1563-0.

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Chen, Hailong, Haijiao Sun, Miao Cheng, and Wuyue Yan. "A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value." Computational Intelligence and Neuroscience 2021 (March 6, 2021): 1–9. http://dx.doi.org/10.1155/2021/6677920.

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Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

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Wang, Shoujin, Wanggen Wan, Tong Qu, and Yanqiu Dong. "Auxiliary Information-Enhanced Recommendations." Applied Sciences 11, no. 19 (September 23, 2021): 8830. http://dx.doi.org/10.3390/app11198830.

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Sequential recommendations have attracted increasing attention from both academia and industry in recent years. They predict a given user’s next choice of items by mainly modeling the sequential relations over a sequence of the user’s interactions with the items. However, most of the existing sequential recommendation algorithms mainly focus on the sequential dependencies between item IDs within sequences, while ignoring the rich and complex relations embedded in the auxiliary information, such as items’ image information and textual information. Such complex relations can help us better understand users’ preferences towards items, and thus benefit from the recommendations. To bridge this gap, we propose an auxiliary information-enhanced sequential recommendation algorithm called memory fusion network for recommendation (MFN4Rec) to incorporate both items’ image and textual information for sequential recommendations. Accordingly, item IDs, item image information and item textual information are regarded as three modalities. By comprehensively modelling the sequential relations within modalities and interaction relations across modalities, MFN4Rec can learn a more informative representation of users’ preferences for more accurate recommendations. Extensive experiments on two real-world datasets demonstrate the superiority of MFN4Rec over state-of-the-art sequential recommendation algorithms.

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Huang, Wenjun, Junyu Chen, and Yue Ding. "Research on Collaborative Filtering Recommendation Based on Trust Relationship and Rating Trust." Frontiers in Business, Economics and Management 1, no. 2 (April 19, 2021): 1–9. http://dx.doi.org/10.54097/fbem.v1i2.13.

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In the Internet age, how to dig out useful information from massive data has become a research hotspot. The emergence of recommendation algorithms effectively solves the problem of information overload, but traditional recommendation algorithms face problems such as data sparseness, cold start, and low accuracy. Later social recommendation algorithms usually only use a single social trust information for recommendation, and the integration of multiple trust relationships lacks an efficient model, which greatly affects the accuracy and reliability of recommendation. This paper proposes a trust-based approach. Recommended algorithm. First, use social trust data to calculate user trust relationships, including user local trust and user global trust. Further based on the scoring data, an implicit trust relationship is calculated, called rating trust, which includes scoring local trust and scoring global trust. Then set the recommendation weight, build the preference relationship between users through user trust and rating trust, and form a comprehensive trust relationship. The trust relationship of social networks is integrated into the probability matrix decomposition model to form an efficient and unified trusted recommendation model TR-PMF. This algorithm is compared with related algorithms on the Ciao and FilmTrust datasets, and the results prove that our method is competitive with other recommendation algorithms.

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Fayyaz, Zeshan, Mahsa Ebrahimian, Dina Nawara, Ahmed Ibrahim, and Rasha Kashef. "Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities." Applied Sciences 10, no. 21 (November 2, 2020): 7748. http://dx.doi.org/10.3390/app10217748.

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Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

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Yang, Gai Zhen. "The Application of Hybrid Recommendation Algorithm in Education Cloud." Applied Mechanics and Materials 551 (May 2014): 670–74. http://dx.doi.org/10.4028/www.scientific.net/amm.551.670.

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When we face large amounts of data, how can we find the most suitable educational resources quickly has become a pressing issue. In this paper, on the basic of comparative study on traditional recommendation algorithms, we use the cloud computing to solve the traditional collaborative filtering algorithms suffer from scalability issues, the proposed algorithm is applied to the combination of recommended teaching cloud platform program, the program according to different recommended by demand different recommendation strategies; open source project Hadoop as a cloud development platform of the algorithm; recommendation algorithm, algorithm on top of Hadoop to achieve improved operating efficiency is relatively high, ideal parallel performance, fully proved the cloud platform and recommended algorithm combining the advantages. The research work on the recommendation system and teaching cloud computing technology applications to provide a useful reference.

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Ladeira, João Damasceno Martins. "The algorithm and the flow: Netflix, machine learning and recommendation algorithms." Intexto, no. 47 (August 6, 2019): 166–84. http://dx.doi.org/10.19132/1807-8583201947.166-184.

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This article discusses the Netflix recommendation system, expecting to understand these techniques as a part of the contemporary strategies for the reorganization of television and audiovisual. It renders problematic a technology indispensable to these suggestions: the tools for artificial intelligence, expecting to infer questions of cultural impact inscribed in this technique. These recommendations will be analyzed in their relationship with the formerly decisive form for the constitution of broadcast: the television flow. The text investigates the meaning such influential tools at the definition of a television based on the manipulation of collections, and not in the predetermined programming, decided previously to the transmission of content. The conclusion explores the consequences of these archives, which concedes to the user a sensation of choice in tension with the mechanical character of those images.

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Puthiya Parambath, Shameem A., and Sanjay Chawla. "Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations." Data Mining and Knowledge Discovery 34, no. 5 (August 3, 2020): 1560–88. http://dx.doi.org/10.1007/s10618-020-00708-6.

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Abstract Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.

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Bazinin, Sagi, and Guy Shani. "Investigating Recommendation Algorithms for Escape Rooms." Vietnam Journal of Computer Science 06, no. 04 (November 2019): 377–88. http://dx.doi.org/10.1142/s2196888819500209.

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An escape room is a physical puzzle solving game, where participants solve a series of riddles within a limited time to exit a locked room. Escape rooms differ in their theme, environment, and difficulty, and people hence often differ on their preferences over escape rooms. As such, recommendation systems can help people in deciding which room to visit. In this paper, we describe the properties of the escape rooms recommendation problem, with respect to other popular recommendation problems. We describe a dataset of reviews collected within a current system. We provide an empirical comparison between a set of recommendation algorithms over two problems, top-N recommendation and rating prediction. In both cases, a KNN method performed the best.

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Zhang, Yiman. "The application of e-commerce recommendation system in smart cities based on big data and cloud computing." Computer Science and Information Systems, no. 00 (2021): 26. http://dx.doi.org/10.2298/csis200917026z.

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In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

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Yu, Fei, An Zeng, Sébastien Gillard, and Matúš Medo. "Network-based recommendation algorithms: A review." Physica A: Statistical Mechanics and its Applications 452 (June 2016): 192–208. http://dx.doi.org/10.1016/j.physa.2016.02.021.

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CACM Staff. "Recommendation algorithms, online privacy, and more." Communications of the ACM 52, no. 5 (May 2009): 10–11. http://dx.doi.org/10.1145/1506409.1506434.

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Ben-Shimon, David, Lior Rokach, Guy Shani, and Bracha Shapira. "Anytime Algorithms for Recommendation Service Providers." ACM Transactions on Intelligent Systems and Technology 7, no. 3 (April 2016): 1–26. http://dx.doi.org/10.1145/2835496.

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Deshpande, Mukund, and George Karypis. "Item-based top- N recommendation algorithms." ACM Transactions on Information Systems 22, no. 1 (January 2004): 143–77. http://dx.doi.org/10.1145/963770.963776.

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Ludewig, Malte, and Dietmar Jannach. "Evaluation of session-based recommendation algorithms." User Modeling and User-Adapted Interaction 28, no. 4-5 (October 1, 2018): 331–90. http://dx.doi.org/10.1007/s11257-018-9209-6.

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Zhang, Zhen, Taile Peng, and Ke Shen. "Overview of Collaborative Filtering Recommendation Algorithms." IOP Conference Series: Earth and Environmental Science 440 (March 19, 2020): 022063. http://dx.doi.org/10.1088/1755-1315/440/2/022063.

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Aditya, T. S., Karthik Rajaraman, and M. Monica Subashini. "Comparative Analysis of Clustering Techniques for Movie Recommendation." MATEC Web of Conferences 225 (2018): 02004. http://dx.doi.org/10.1051/matecconf/201822502004.

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Movie recommendation is a subject with immense ambiguity. A person might like a movie but not a very similar movie. The present recommending systems focus more on just few parameters such as Director, cast and genre. A lot of Power intensive methods such as Deep Convolutional Neural Network (CNN) has been used which demands the use of Graphics processors that require more energy. We try to accomplish the same task using lesser Energy consuming algorithms such as clustering techniques. In this paper, we try to create a more generalized list of similar movies in order to provide the user with more variety of movies which he/she might like, using clustering algorithms. We will compare how choosing different parameters and number of features affect the cluster's content. Also, compare how different algorithms such as K-mean, Hierarchical, Birch and mean shift clustering algorithms give a varied result and conclude which method will suit for which scenarios of movie recommendations. We also conclude on which algorithm clusters stray data points more efficiently and how different algorithms provide different advantages and disadvantages.

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Tianxing, Man, Ildar Raisovich Baimuratov, and Natalia Alexandrovna Zhukova. "A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing." International Journal of Embedded and Real-Time Communication Systems 10, no. 4 (October 2019): 20–38. http://dx.doi.org/10.4018/ijertcs.2019100102.

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With the development of the Big Data, data analysis technology has been actively developed, and now it is used in various subject fields. More and more non-computer professional researchers use machine learning algorithms in their work. Unfortunately, datasets can be messy and knowledge cannot be directly extracted, which is why they need preprocessing. Because of the diversity of the algorithms, it is difficult for researchers to find the most suitable algorithm. Most of them choose algorithms through their intuition. The result is often unsatisfactory. Therefore, this article proposes a recommendation system for data processing. This system consists of an ontology subsystem and an estimation subsystem. Ontology technology is used to represent machine learning algorithm taxonomy, and information-theoretic based criteria are used to form recommendations. This system helps users to apply data processing algorithms without specific knowledge from the data science field.

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Ranjan, Ankita, and Vinay M. "A Comparison of recommendation algorithms based on use of linked data and cloud." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 126. http://dx.doi.org/10.14419/ijet.v7i2.6.10137.

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Recommendation generation is a critical need in today's time. With the advent of big data and the increasing number of users, generation of most suitable recommendation is essential. There are many issues already associated with recommendations such as data acquisition, scalability, etc.. Moreover, the users today look to get best recommendations at the minimum effort on their side. Thus it becomes difficult to manage such huge amount of information, extract the needed data and present it to the user with least user involvement. In this research, we surveyed some recommendation algorithms and analyze their applications on an open cloud server which uses linked data to generate automated recommendations.

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Amer Jaafar, Basim, Methaq Talib Gaata, and Mahdi Nsaif Jasim. "Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (September 1, 2020): 1635. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1635-1642.

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<p>The recommendation system is an intelligent system gives recommendations to users to discover the best interesting items. The purpose of this proposed recommendation system is to develop a system to find the best electrical devices according to weather conditions and user preferences. The proposed solution relies on the characteristics of electrical appliances and their suitability to weather conditions in any city. The proposed solution is the first recommendation system combines devices properties, weather conditions, and user preferences using a new combination of algorithms. The clustering algorithms are the most applicable in the field of recommendation system. The proposed solution relies on a combination of Elbow method, proposed modified K-means and Silhouette algorithm to find the best number of clusters before starting the clustering process. Then calculate the weights for each cluster and compare them with the weather weights to find the required clusters sorted from the near to far according to a computed threshold. The empirical results showed that the proposed solution demonstrated a 94% accuracy to match the characteristics of the recommended devices with the climatic characteristics of the region and user preferences. The accuracy is measured using Silhouette algorithm.</p>

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Ciesielczyk, Michał, Andrzej Szwabe, and Mikołaj Morzy. "On Efficient Link Recommendation in Social Networks Using Actor-Fact Matrices." Scientific Programming 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/450215.

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Link recommendation is a popular research subject in the field of social network analysis and mining. Often, the main emphasis is put on the development of new recommendation algorithms, semantic enhancements to existing solutions, design of new similarity measures, and so forth. However, relatively little scientific attention has been paid to the impact that various data representation models have on the performance of recommendation algorithms. And by performance we do not mean the time or memory efficiency of algorithms, but the precision and recall of recommender systems. Our recent findings unanimously show that the choice of network representation model has an important and measurable impact on the quality of recommendations. In this paper we argue that the computation quality of link recommendation algorithms depends significantly on the social network representation and we advocate the use of actor-fact matrix as the best alternative. We verify our findings using several state-of-the-art link recommendation algorithms, such as SVD, RSVD, and RRI using both single-relation and multirelation dataset.

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Long, Fei. "Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems." Complexity 2020 (October 5, 2020): 1–11. http://dx.doi.org/10.1155/2020/7945417.

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With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.

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Pankong, Nichakorn, and Somchai Prakancharoen. "Combining Algorithms for Recommendation System on Twitter." Advanced Materials Research 403-408 (November 2011): 3688–92. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3688.

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Twitter has rapidly increased in popularity over the past few years. So, we have focused on Twitter as it has a large scale of data which is increasingly difficult to search through. In this paper, we propose recommendations for content on Twitter. We explored four dimensions in designing such as: topic relevance of content sources, the content candidate set for users, social voting and Meta data mapping. We implemented 24 algorithms for analysis of 12,000 records for three domains as follows: entertainment, stock exchange and smart phone in the design space. The best performing algorithm improved the percentage of correct matching interesting content to 23.86%.

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Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (February 2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.

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In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).

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Zhang, Zhijun, Gongwen Xu, and Pengfei Zhang. "Research on E-Commerce Platform-Based Personalized Recommendation Algorithm." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5160460.

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Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.

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Dewi, Ratih Kartika, Eriq Muhammad Adams Jonemaro, Agi Putra Kharisma, Najla Alia Farah, and Mury Fajar Dewantoro. "TOPSIS for mobile based group and personal decision support system." Register: Jurnal Ilmiah Teknologi Sistem Informasi 7, no. 1 (February 15, 2021): 43. http://dx.doi.org/10.26594/register.v7i1.2140.

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Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

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Sun, Ming Yang, Wei Feng Sun, Xi Dong Liu, and Lei Xue. "A Novel Personalized Filtering Recommendation Algorithm Based on Collaborative Tagging." Advanced Materials Research 186 (January 2011): 621–25. http://dx.doi.org/10.4028/www.scientific.net/amr.186.621.

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Recommendation algorithms suffer the quality from the huge and sparse dataset. Memory-based collaborative filtering method has addressed the problem of sparsity by predicting unrated values. However, this method increases the computational complexity, sparsity and expensive complexity of computation are trade-off. In this paper, we propose a novel personalized filtering (PF) recommendation algorithm based on collaborative tagging, which weights the feature of tags that show latent personal interests and constructs a top-N tags set to filter out the undersized and dense dataset. The PF recommendation algorithm can track the changes of personal interests, which is an untilled field for previous studies. The results of empirical experiments show that the sparsity level of PF recommendation algorithm is much lower, and it is more computationally economic than previous algorithms.

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Chen, Xi, Yangsiyi Lu, Yuehai Wang, and Jianyi Yang. "CMBF: Cross-Modal-Based Fusion Recommendation Algorithm." Sensors 21, no. 16 (August 4, 2021): 5275. http://dx.doi.org/10.3390/s21165275.

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A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-Modal-Based Fusion Recommendation Algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results.

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Xu, Min, Yuan Zhang, Mei Qi Fang, and Ning Li. "An Intelligent Personalized Learning Model Based on Community Discovery Method." Advanced Materials Research 159 (December 2010): 248–51. http://dx.doi.org/10.4028/www.scientific.net/amr.159.248.

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In this paper, we proposed a model of support personalized learning based on SGCL (Social Group Collaborative Learning System). In the model, we provide two algorithms to discover knowledge communities. Based on the community discovery result and system recommendation policy, we give our user the recommendation suggestions to help them to construct their personalized knowledge structure. The paper mainly introduce these algorithms, the AG algorithm based on aggregation and the KC algorithm based on K-Clique model, which are algorithms to discover knowledge communities in SGCL.

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Aditya, Eka Surya, and Wikan Danar Sunindyo. "Development of a Land Transportation Recommendation System Using the Hill Climbing Algorithm." International Journal of Engineering and Applied Science Research 1, no. 1 (July 30, 2020): 7. http://dx.doi.org/10.26418/ijeasr.v1i1.41312.

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Communities in big cities often encounter problems in using public transportation due to difficulties in accessing available information. The information is not well integrated and scattered in various places. For this reason, an information and recommendation system is needed to facilitate the public in choosing the right mode of land transportation. The recommendation system can be built using the Hill Climbing algorithm. In this paper, I explain the development of a public land transportation recommendation system using three types of Hill Climbing Algorithms. The results of the recommendations are analyzed based on the complexity of asymptotic time, space complexity, and the quality of the results.

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Chen, Jinpeng, Yu Liu, and Deyi Li. "Enhancing Recommender Diversity Using Gaussian Cloud Transformation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no. 04 (August 2015): 521–44. http://dx.doi.org/10.1142/s0218488515500233.

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The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

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Kim, Hyun Suk, Sijia Yang, Minji Kim, Brett Hemenway, Lyle Ungar, and Joseph N. Cappella. "An Experimental Study of Recommendation Algorithms for Tailored Health Communication." Computational Communication Research 1, no. 1 (October 1, 2019): 103–29. http://dx.doi.org/10.5117/ccr2019.1.005.sukk.

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Abstract Recommendation algorithms are widely used in online cultural markets to provide personalized suggestions for products like books and movies. At the heart of the commercial success of recommendation algorithms is their ability to make an accurate prediction of a target person’s preferences for previously unseen items. Can these algorithms also be used to predict which health messages an individual will evaluate favorably, and thereby provide effective tailored communication to the person? Although there is evidence that message tailoring enhances persuasion, little research has examined the effectiveness of recommendation algorithms for tailored health interventions aimed at promoting behavior change. We developed a message tailoring algorithm to select smoking-related public service announcements (PSAs) for smokers, and experimentally test its effectiveness in predicting a target smoker’s evaluations of PSAs and encouraging smoking cessation. The tailoring algorithm was constructed using multiple levels of data on smokers’ PSA rating history, individual differences, content features of the PSAs, and other smokers’ PSA ratings. We conducted a longitudinal online experiment to examine its efficacy in comparison to two non-tailored methods: “best in show” (choosing messages most preferred by other smokers) and “off the shelf” (random selection from eligible ads). The results showed that the tailoring algorithm produced more accurate predictions of smokers’ message evaluations than the simple-average method used for the “best in show” approach. Smokers who viewed PSAs recommended by the tailoring algorithm were more likely than those receiving a random set to evaluate the PSAs favorably and quit smoking. There was no significant difference between the “best in show” and “off the shelf” methods in message assessment and quitting behavior.

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ZENG, WEI, MING-SHENG SHANG, QIAN-MING ZHANG, LINYUAN LÜ, and TAO ZHOU. "CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?" International Journal of Modern Physics C 21, no. 10 (October 2010): 1217–27. http://dx.doi.org/10.1142/s0129183110015786.

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Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

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Seng, Dewen, Jiaxin Liu, Xuefeng Zhang, Jing Chen, and Xujian Fang. "Top-N Recommendation Based on Mutual Trust and Influence." International Journal of Computers Communications & Control 14, no. 4 (August 5, 2019): 540–56. http://dx.doi.org/10.15837/ijccc.2019.4.3578.

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To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach.

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Chen, Jianrui, Zhihui Wang, Tingting Zhu, and Fernando E. Rosas. "Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism." Complexity 2020 (July 7, 2020): 1–19. http://dx.doi.org/10.1155/2020/5206087.

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The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

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Journal articles on the topic 'Recommendation algorithms'

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