Relevant bibliographies by topics / COLLABORATIVE FILTERING ALGORITHMS

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Academic literature on the topic 'COLLABORATIVE FILTERING ALGORITHMS'

Author: Grafiati
Published: 26 October 2023

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Journal articles on the topic "COLLABORATIVE FILTERING ALGORITHMS"

1

Ben Kharrat, Firas, Aymen Elkhleifi, and Rim Faiz. "Improving Collaborative Filtering Algorithms." International Journal of Knowledge Society Research 7, no. 3 (2016): 99–118. http://dx.doi.org/10.4018/ijksr.2016070107.

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This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.
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Cacheda, Fidel, Víctor Carneiro, Diego Fernández, and Vreixo Formoso. "Comparison of collaborative filtering algorithms." ACM Transactions on the Web 5, no. 1 (2011): 1–33. http://dx.doi.org/10.1145/1921591.1921593.

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3

Zhou, Li Juan, Ming Sheng Xu, and Hai Jun Geng. "Improved Attack-Resistant Collaborative Filtering Algorithm." Key Engineering Materials 460-461 (January 2011): 439–44. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.439.

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Collaborative filtering is very effective in recommendation systems. But the recently researches have proved the collaborative filtering is significant vulnerable in the face of profile injection attacks. Profile injection attacks can be identified to some attack models. The attacker can easily bias the prediction of the system based on collaborative filtering algorithms. In this paper, an improved algorithm based on Singular Value Decomposition is proposed. Some dimensions are chosen by the improved algorithm to find capture latent relationships between customers and products. In addition, the robustness of the algorithm is improved by the way. Several experiments are conducted. The results suggest that the proposed algorithm has advantages both in robust and stable over previous algorithms.
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Wu, Xinyi. "Comparison Between Collaborative Filtering and Content-Based Filtering." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 480–89. http://dx.doi.org/10.54097/hset.v16i.2627.

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With the rapid development of Internet technology nowadays, how to quickly obtain the effective information needed by users has become the key point of the scientific and technological academia. Therefore, various kinds of recommendation algorithms have been invented. Based on the previous research, this paper introduces the most famous and widely used recommendation algorithms among many recommendation systems, which are collaborative filtering and content-based filtering. In this paper, the core ideas and operation principles of the two algorithms are introduced in detail. In addition, by describing the steps of these two algorithms gradually and analyzing their processes step by step, we can accurately analyze and summarize their advantages and disadvantages respectively. And on this basis, the respective areas which they are good at are mentioned. Moreover, this paper points out the shortcomings and limitations that still exist at present, and the direction for further improvement in the future. Finally, at the end of the paper, there are some overall comparation and summation about the two algorithms. And the hot research points of them in the future are discussed.
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5

Jalili, Mahdi. "A Survey of Collaborative Filtering Recommender Algorithms and Their Evaluation Metrics." International Journal of System Modeling and Simulation 2, no. 2 (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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6

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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7

Jing, Hui. "Application of Improved K-Means Algorithm in Collaborative Recommendation System." Journal of Applied Mathematics 2022 (December 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/2213173.

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With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K-means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm. The experimental results show that the MAE value of the new fitness function is 0.767 on average, which has good separation and compactness in clustering effect. It shows high search accuracy and speed. Compared with the original collaborative filtering algorithm, the average absolute error value of this algorithm is low, and the running time is only 50 s. It improves the recommendation quality and ensures the recommendation efficiency, providing a new research path for the improvement of collaborative filtering recommendation algorithm.
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8

Jiang, Tong Qiang, and Wei Lu. "Improved Slope One Algorithm Based on Time Weight." Applied Mechanics and Materials 347-350 (August 2013): 2365–68. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2365.

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Collaborative filtering is regarded as the most prevailing techniques for recommendation system. Slope one is a family of algorithms used for collaborative filtering. It is the simplest form of non-trivial item-based collaborative filtering based on ratings. But all the family of use CF algorithms ignores one important problem: ratings produced at different times are weighted equally. It means that they cant catch users different attitudes at different time. So in this paper, we present a new algorithm, which could assign different weights for items at different time. Finally, we experimentally evaluate our approach and compare it to the original Slope One. The experiment shows that the new slope one algorithms can improve the precision
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9

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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10

Kourtiche, Ali, and Mohamed Merabet. "Collaborative Filtering Technical Comparison in Implicit Data." International Journal of Knowledge-Based Organizations 11, no. 4 (2021): 1–24. http://dx.doi.org/10.4018/ijkbo.2021100101.

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Recommendation systems have become a necessity due to the mass of information accumulated for each site. For this purpose, there are several methods including collaborative filtering and content-based filtering. For each approach there is a vast list of procedural choices. The work studies the different methods and algorithms in the field of collaborative filtering recommendation. The objective of the work is to implement these algorithms in order to compare the different performances of each one; the tests were carried out in two datasets, book crossing and Movieslens. The use of a data set benchmark is crucial for the proper evaluation of collaborative filtering algorithms in order to draw a conclusion on the performance of the algorithms.
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