Relevant bibliographies by topics / Recommendation algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Recommendation algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Recommendation algorithms"

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VASILOUDIS, THEODOROS. "Extending recommendation algorithms bymodeling user context." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-156306.

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Recommender systems have been widely adopted by onlinee-commerce websites like Amazon and music streaming services like Spotify. However, most research efforts have not sufficiently considered the context in which recommendations are made, especially when the input is implicit. In this work, we investigate the value of including contextual information like day-of-week in collaborative filtering recommender systems. For the investigation, we first implemented two algorithms, namely contextual prefiltering and contextual post-filtering. Then, we evaluated these algorithms with user data collected from Spotify. Experiment results show that the pre-filtering algorithm shows some promise against an item similarity baseline, indicating that further investigation could be rewarding. The post-filtering algorithm underperforms a popularity-derived baseline, due to information loss in the recommendationprocess.
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Viviani, Giovanni. "Optimizing modern code review through recommendation algorithms." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58757.

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Software developers have many tools at their disposal that use a variety of sophisticated technology, such as static analysis and model checking, to help find defects before software is released. Despite the availability of such tools, software development still relies largely on human inspection of code to find defects. Many software development projects use code reviews as a means to ensure this human inspection occurs before a commit is merged into the system. Known as modern code review, this approach is based on tools, such as Gerrit, that help developers track commits for which review is needed and that help perform reviews asynchronously. As part of this approach, developers are often presented with a list of open code reviews requiring attention. Existing code review tools simply order this list of open reviews based on the last update time of the review; it is left to a developer to find a suitable review on which to work from a long list of reviews. In this thesis, we present an investigation of four algorithms that recommend an ordering of the list of open reviews based on properties of the reviews. We use a simulation study over a dataset of six projects from the Eclipse Foundation to show that an algorithm based on ordering reviews from least lines of code modified in the changes to be reviewed to most lines of code modified out performs other algorithms. This algorithm shows promise for eliminating stagnation of reviews and optimizing the average duration reviews are open.
Science, Faculty of
Computer Science, Department of
Graduate
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Casey, Walker Evan. "Scalable Collaborative Filtering Recommendation Algorithms on Apache Spark." Scholarship @ Claremont, 2014. http://scholarship.claremont.edu/cmc_theses/873.

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Collaborative filtering based recommender systems use information about a user's preferences to make personalized predictions about content, such as topics, people, or products, that they might find relevant. As the volume of accessible information and active users on the Internet continues to grow, it becomes increasingly difficult to compute recommendations quickly and accurately over a large dataset. In this study, we will introduce an algorithmic framework built on top of Apache Spark for parallel computation of the neighborhood-based collaborative filtering problem, which allows the algorithm to scale linearly with a growing number of users. We also investigate several different variants of this technique including user and item-based recommendation approaches, correlation and vector-based similarity calculations, and selective down-sampling of user interactions. Finally, we provide an experimental comparison of these techniques on the MovieLens dataset consisting of 10 million movie ratings.
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Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.

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Représenter l'information décrivant la musique est une activité complexe, qui implique différentes sous-tâches. Ce manuscrit de thèse porte principalement sur la musique classique et étudie comment représenter et exploiter ses informations. L'objectif principal est l'étude de stratégies de représentation et de découverte des connaissances appliquées à la musique classique, dans des domaines tels que la production de base de connaissances, la prédiction de métadonnées et les systèmes de recommandation. Nous proposons une architecture pour la gestion des métadonnées de musique à l'aide des technologies du Web Sémantique. Nous introduisons une ontologie spécialisée et un ensemble de vocabulaires contrôlés pour les différents concepts spécifiques à la musique. Ensuite, nous présentons une approche de conversion des données, afin d’aller au-delà de la pratique bibliothécaire actuellement utilisée, en s’appuyant sur des règles de mapping et sur l’interconnexion avec des vocabulaires contrôlés. Enfin, nous montrons comment ces données peuvent être exploitées. En particulier, nous étudions des approches basées sur des plongements calculés sur des métadonnées structurées, des titres et de la musique symbolique pour classer et recommander de la musique. Plusieurs applications de démonstration ont été réalisées pour tester les approches et les ressources précédentes
Representing the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realised for testing the previous approaches and resources
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Asebedo, Antonio Ray. "Development of sensor-based nitrogen recommendation algorithms for cereal crops." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/19229.

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Doctor of Philosophy
Department of Agronomy
David B. Mengel
Nitrogen (N) management is one of the most recognizable components of farming both within and outside the world of agriculture. Interest over the past decade has greatly increased in improving N management systems in corn (Zea mays) and winter wheat (Triticum aestivum) to have high NUE, high yield, and be environmentally sustainable. Nine winter wheat experiments were conducted across seven locations from 2011 through 2013. The objectives of this study were to evaluate the impacts of fall-winter, Feekes 4, Feekes 7, and Feekes 9 N applications on winter wheat grain yield, grain protein, and total grain N uptake. Nitrogen treatments were applied as single or split applications in the fall-winter, and top-dressed in the spring at Feekes 4, Feekes 7, and Feekes 9 with applied N rates ranging from 0 to 134 kg ha[superscript]-1. Results indicate that Feekes 7 and 9 N applications provide more optimal combinations of grain yield, grain protein levels, and fertilizer N recovered in the grain when compared to comparable rates of N applied in the fall-winter or at Feekes 4. Winter wheat N management studies from 2006 through 2013 were utilized to develop sensor-based N recommendation algorithms for winter wheat in Kansas. Algorithm RosieKat v.2.6 was designed for multiple N application strategies and utilized N reference strips for establishing N response potential. Algorithm NRS v1.5 addressed single top-dress N applications and does not require a N reference strip. In 2013, field validations of both algorithms were conducted at eight locations across Kansas. Results show algorithm RK v2.6 consistently provided highly efficient N recommendations for improving NUE, while achieving high grain yield and grain protein. Without the use of the N reference strip, NRS v1.5 performed statistically equal to the KSU soil test N recommendation in regards to grain yield but with lower applied N rates. Six corn N fertigation experiments were conducted at KSU irrigated experiment fields from 2012 through 2014 to evaluate the previously developed KSU sensor-based N recommendation algorithm in corn N fertigation systems. Results indicate that the current KSU corn algorithm was effective at achieving high yields, but has the tendency to overestimate N requirements. To optimize sensor-based N recommendations for N fertigation systems, algorithms must be specifically designed for these systems to take advantage of their full capabilities, thus allowing implementation of high NUE N management systems.
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Li, Lei. "Next Generation of Recommender Systems: Algorithms and Applications." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1446.

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Personalized recommender systems aim to assist users in retrieving and accessing interesting items by automatically acquiring user preferences from the historical data and matching items with the preferences. In the last decade, recommendation services have gained great attention due to the problem of information overload. However, despite recent advances of personalization techniques, several critical issues in modern recommender systems have not been well studied. These issues include: (1) understanding the accessing patterns of users (i.e., how to effectively model users' accessing behaviors); (2) understanding the relations between users and other objects (i.e., how to comprehensively assess the complex correlations between users and entities in recommender systems); and (3) understanding the interest change of users (i.e., how to adaptively capture users' preference drift over time). To meet the needs of users in modern recommender systems, it is imperative to provide solutions to address the aforementioned issues and apply the solutions to real-world applications. The major goal of this dissertation is to provide integrated recommendation approaches to tackle the challenges of the current generation of recommender systems. In particular, three user-oriented aspects of recommendation techniques were studied, including understanding accessing patterns, understanding complex relations and understanding temporal dynamics. To this end, we made three research contributions. First, we presented various personalized user profiling algorithms to capture click behaviors of users from both coarse- and fine-grained granularities; second, we proposed graph-based recommendation models to describe the complex correlations in a recommender system; third, we studied temporal recommendation approaches in order to capture the preference changes of users, by considering both long-term and short-term user profiles. In addition, a versatile recommendation framework was proposed, in which the proposed recommendation techniques were seamlessly integrated. Different evaluation criteria were implemented in this framework for evaluating recommendation techniques in real-world recommendation applications. In summary, the frequent changes of user interests and item repository lead to a series of user-centric challenges that are not well addressed in the current generation of recommender systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective recommendation framework.
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Dhumal, Sayali. "WEB APPLICATION FOR GRADUATE COURSE RECOMMENDATION SYSTEM." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/605.

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The main aim of the course advising system is to build a course recommendation path for students to help them plan courses to successfully graduate on time. The recommendation path displays the list of courses a student can take in each quarter from the first quarter after admission until the graduation quarter. The courses are filtered as per the student’s interest obtained from a questionnaire asked to the student. The business logic involves building the recommendation algorithm. Also, the application is functionality-tested end-to-end by using nightwatch.js which is built on top of node.js. Test cases are written for every module and implemented while building the application.
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Nicol, Olivier. "Data-driven evaluation of contextual bandit algorithms and applications to dynamic recommendation." Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10211/document.

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Ce travail de thèse a été réalisé dans le contexte de la recommandation dynamique. La recommandation est l'action de fournir du contenu personnalisé à un utilisateur utilisant une application, dans le but d'améliorer son utilisation e.g. la recommandation d'un produit sur un site marchant ou d'un article sur un blog. La recommandation est considérée comme dynamique lorsque le contenu à recommander ou encore les goûts des utilisateurs évoluent rapidement e.g. la recommandation d'actualités. Beaucoup d'applications auxquelles nous nous intéressons génèrent d'énormes quantités de données grâce à leurs millions d'utilisateurs sur Internet. Néanmoins, l'utilisation de ces données pour évaluer une nouvelle technique de recommandation ou encore comparer deux algorithmes de recommandation est loin d'être triviale. C'est cette problématique que nous considérons ici. Certaines approches ont déjà été proposées. Néanmoins elles sont très peu étudiées autant théoriquement (biais non quantifié, borne de convergence assez large...) qu'empiriquement (expériences sur données privées). Dans ce travail nous commençons par combler de nombreuses lacunes de l'analyse théorique. Ensuite nous discutons les résultats très surprenants d'une expérience à très grande échelle : une compétition ouverte au public que nous avons organisée. Cette compétition nous a permis de mettre en évidence une source de biais considérable et constamment présente en pratique : l'accélération temporelle. La suite de ce travail s'attaque à ce problème. Nous montrons qu'une approche à base de bootstrap permet de réduire mais surtout de contrôler ce biais
The context of this thesis work is dynamic recommendation. Recommendation is the action, for an intelligent system, to supply a user of an application with personalized content so as to enhance what is refered to as "user experience" e.g. recommending a product on a merchant website or even an article on a blog. Recommendation is considered dynamic when the content to recommend or user tastes evolve rapidly e.g. news recommendation. Many applications that are of interest to us generates a tremendous amount of data through the millions of online users they have. Nevertheless, using this data to evaluate a new recommendation technique or even compare two dynamic recommendation algorithms is far from trivial. This is the problem we consider here. Some approaches have already been proposed. Nonetheless they were not studied very thoroughly both from a theoretical point of view (unquantified bias, loose convergence bounds...) and from an empirical one (experiments on private data only). In this work we start by filling many blanks within the theoretical analysis. Then we comment on the result of an experiment of unprecedented scale in this area: a public challenge we organized. This challenge along with a some complementary experiments revealed a unexpected source of a huge bias: time acceleration. The rest of this work tackles this issue. We show that a bootstrap-based approach allows to significantly reduce this bias and more importantly to control it
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Yang, Fan [Verfasser]. "Analysis, Design and Implementation of Personalized Recommendation Algorithms Supporting Self-organized Communities / Fan Yang." Hagen : Fernuniversität Hagen, 2009. http://d-nb.info/1034265822/34.

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Qadeer, Shahab. "Integration of Recommendation and Partial Reference Alignment Algorithms in a Session based Ontology Alignment System." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-73135.

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SAMBO is a system to assist users for alignment and merging of two ontologies (i.e. to find inter-ontology relationship). The user performs an alignment process with the help of mapping suggestions. The objective of the thesis work is to extend the existing system with new components; multiple sessions, integration of an ontology alignment strategy, recommendation system, integration of a system that can use results from previous sessions, and integration of partial reference alignment (PRA) that can be used to filter mapping suggestions. Most of the theoretical work existed, but it was important to study and implement, how these components can be integrated in the system, and how they can work together.
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Books on the topic "Recommendation algorithms"

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Gündüz-Ögüdücü, Şule. Web page recommendation models: Theory and algorithms. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Boston (Mass.). School Committee. Recommendation to implement a new BPS assignment algorithm. [Massachusetts?: BPS Strategic Planning Team?], 2005.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Mandra, Yuliya, Elena Semencova, Sergey Griroriev, N. Gegalina, Elena Svetlakova, Maria Vlasova, Yuriy Boldyrev, Anastasiya Kotikova, Aleksandr Ivashov, and Aleksandr Legkih. MODERN METHODS OF COMPLEX TREATMENT OF PATIENTS WITH HERPES SIMPLEX LIPS. ru: TIRAZH Publishing House, 2019. http://dx.doi.org/10.18481/textbook_5dfa340500ebf6.85792235.

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The training manual is devoted to the problem of herpetic infection in dentistry and was developed taking into account world scientific and clinical practice, experience working on clinical recommendations of the Ministry of Health of the Russian Federation, as well as experimental, laboratory and clinical data obtained by the authors. This manual presents materials related to modern ideas about the etiology and pathogenesis of herpetic infection, modern diagnostic methods are highlighted, and current complex treatment algorithms are proposed, and clinical cases are presented. Recommended as a guide for practitioners of various specialties, clinical residents, senior students.
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Ulyanina, Olga, Azalia Zinatullina, and Elena Lyubka. Countering terrorism: psychological assistance to students and the formation of a safe type of personality. ru: Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02048-7.

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The manual describes a program of psychological support for students exposed to the ideology of terrorism or falling under the influence of this ideology. In this regard, the content of educational, psychodiagnostic, correctional and developmental stages of its implementation is revealed. The paper presents an algorithm for conducting psychological counseling with students and recommendations for parents on psychological support for children exposed to the ideology of terrorism. The practical tools described in the manual can be used in the framework of preventive and corrective work with participants in the educational process. The developed materials are addressed to education administrators, teachers, educational psychologists of educational organizations and parents.
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Cevelev, Aleksandr. The economy and material management on a railway transport. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1085329.

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In the textbook in an accessible form presented and discussed the development of the economy and the inventory management of railway transport in the new economic environment. For the first time in Russian literature, made a theoretical attempt at a comprehensive review of the effectiveness of, and satisfaction of needs in material resources structural divisions, subsidiaries and affiliates of JSC "RZD". According to the results of theoretical research, innovative and production potential of the supply system of railway transport the main directions and methods of transformation of the restructuring process under the corporate changes of JSC "RZD", positioned value system of logistics of rail transport, a comprehensive approach to the development of systems of balanced indicators of supply and prompt handling of material resources. Recommendations for the implementation of the developed algorithms and models are long term in nature and are based on the concept of logistics management improve business processes, system logistics. For students and teachers, workers of enterprises of railway transport, and others interested in questions of transport Economics.
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Sokol'skaya, Elena, and Boris Kochurov. Geoecology of the city: models of environmental quality. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1205961.

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The monograph examines the features of studying the geoecological state of urbanized territories, reveals the use of integrated assessment and mapping in urban diagnostics, and finds a solution to geoecological problems on the example of world cities that are leading in the rating for quality of life. The components of an information and analytical model of the urban environment for assessing the geoecological situation are described; an algorithm for a comprehensive study of the geoecological state aimed at an adequate assessment of the quality of the urban environment. Special attention is paid to the methodology of geoecological assessment of the quality of the urban environment based on multifactor modeling, which allows making recommendations for improving the comfort of living of the population. It is intended for a wide range of specialists in the field of geoecology of the city, and can also be used as a textbook for students of environmental, natural-geographical, engineering specialties.
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Cevelev, Aleksandr. Material management of railway transport. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064961.

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In the monograph reviewed the development of the inventory management of railway transport in the new economic environment of market economy. According to the results of theoretical research, innovative and production potential of the supply system of railway transport the main directions and methods of transformation of the restructuring process under the corporate changes of JSC "RZD", positioned value system of the logistics of railway transportation, and developed a classification model used logistical resources. Evaluation of activity of structural divisions of Russian Railways supply is proposed to be viewed through an integrated and comprehensive approach to the development of systems of balanced indicators of supply and prompt handling of material resources, the implementation of which allows to distribute the strategic objectives of the company "Russian Railways" activities in the system of logistics of the Railways and also to involve in economic circulation of excessive and unused inventories of material and technical resources and efficiently reallocate them among enterprises at the site of the railway. Recommendations for the implementation of the developed algorithms and models are long term in nature and are based on the concept of logistics management and improve the business processes of the logistics system. Will be useful for managers and specialists of directorates of logistics of Russian Railways supply, undergraduates and graduate students interested in the economy of railway transport.
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Cevelev, Aleksandr. Material and technical support of railway transport. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1417121.

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The textbook covers topical issues of the implementation of the policy of JSC "Russian Railways" in the field of material and technical support of structural divisions of railway transport. Based on the results of theoretical studies of the system management of business processes of material and technical support of railway transport, the diversification of the activities of supply authorities, the concept was formulated and a tree of management goals was developed, as well as the formalization of the existing business processes of the MTO system for the current period was carried out and the supply efficiency management system was considered in accordance with the SRT of JSC Russian Railways. Recommendations on the implementation of the developed algorithms and models are of a long-term nature and will improve the efficiency of the system of providing services for providing material and technical resources to structural divisions and enterprises of railway transport. Meets the federal state educational standards of the latest generation in the areas of training: 38.03.01 "Economics", 38.03.02 " Management "(profiles: "Corporate Management", "Logistics and Supply Chain Management"). It is intended for students of railway transport universities of economic and engineering specialties, as well as for managers and specialists of material and technical support of JSC "Russian Railways" who are interested in the issues of material, information and financial support of railway transport.
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Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers, 2018.

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Book chapters on the topic "Recommendation algorithms"

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Felfernig, Alexander, Ludovico Boratto, Martin Stettinger, and Marko Tkalčič. "Algorithms for Group Recommendation." In SpringerBriefs in Electrical and Computer Engineering, 27–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75067-5_2.

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Guy, Ido. "Algorithms for Social Recommendation." In Handbook of Human Computation, 649–71. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8806-4_52.

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Kakarla, Ramcharan, Sundar Krishnan, and Sridhar Alla. "Unsupervised Learning and Recommendation Algorithms." In Applied Data Science Using PySpark, 251–98. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6500-0_7.

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Vieira, Armando, and Bernardete Ribeiro. "Recommendation Algorithms and E-commerce." In Introduction to Deep Learning Business Applications for Developers, 171–84. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3453-2_7.

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Shah, Samkit, and Harshal Trivedi. "Social Media Analytics and Mutual Fund Recommendation." In Algorithms for Intelligent Systems, 287–303. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5077-5_26.

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Amrutkar, Saurabh, Shantanu Mahakal, and Ajay Naidu. "Recommender Systems for University Elective Course Recommendation." In Algorithms for Intelligent Systems, 247–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4862-2_27.

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Kharroubi, Sahraoui, Youcef Dahmani, and Omar Nouali. "Item-Share Propagation Link Applying for Recommendation." In Software Engineering and Algorithms, 620–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77442-4_52.

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Gupta, Shefali, and Meenu Dave. "A Hybrid Recommendation System for E-commerce." In Algorithms for Intelligent Systems, 229–36. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3246-4_18.

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Hwang, Chein-Shung, Yi-Ching Su, and Kuo-Cheng Tseng. "Using Genetic Algorithms for Personalized Recommendation." In Computational Collective Intelligence. Technologies and Applications, 104–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16732-4_12.

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Yan, Biwei, Jiguo Yu, Yue Wang, Qiang Guo, Baobao Chai, and Suhui Liu. "Blockchain-Based Service Recommendation Supporting Data Sharing." In Wireless Algorithms, Systems, and Applications, 580–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59016-1_48.

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Conference papers on the topic "Recommendation algorithms"

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Felfernig, A., and G. Ninaus. "Group recommendation algorithms for requirements prioritization." In 2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE). IEEE, 2012. http://dx.doi.org/10.1109/rsse.2012.6233412.

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Chen, Du, Yuming Deng, Guangrui Ma, Hao Ge, Yunwei Qi, Ying Rong, Xun Zhang, and Huan Zheng. "Inventory Based Recommendation Algorithms." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378261.

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Bai, Xinxin, Jinlong Wu, Haifeng Wang, Jun Zhang, Wenjun Yin, and Jin Dong. "Recommendation algorithms for implicit information." In 2011 IEEE International Conference on Service Operations and Logistics and Informatics (SOLI). IEEE, 2011. http://dx.doi.org/10.1109/soli.2011.5986556.

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Vargas, Dalton L., Jones Granatyr, Jeferson Knop, and Cleber De Almeida. "Product Recommendation Using Classification Algorithms." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4462.

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Com a expansão da internet pelo mundo, o início do consumo em dispositivos móveis e a propagação doe-commerce, sistemas de recomendação e mineração de dados, tornaram-se um tema extremamente atrativo. Portanto, é necessário filtrar, priorizar e fornecer informações relevantes, a fim de enfrentar com eficácia o problema da sobrecarga de informações. Assim, o objetivo deste trabalho é trazer contribuições científicas para a área de Inteligência Artificial, no sentido de demonstrar como recomendar produtos, para usuários que não possuem conhecimento técnico específico, através de técnicas de classificação. Nossa proposta é avaliada pela precisão das recomendações obtidas através dos algoritmos de classificação aplicados as bases de dados de treinamento.
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Burbach, Laura, Johannes Nakayama, Nils Plettenberg, Martina Ziefle, and André Calero Valdez. "User preferences in recommendation algorithms." In RecSys '18: Twelfth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240323.3240393.

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Wang, Mengsha, Yingyuan Xiao, Wenguang Zheng, Xu Jiao, and Ching-Hsien Hsu. "Tag-Based Personalized Music Recommendation." In 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN). IEEE, 2018. http://dx.doi.org/10.1109/i-span.2018.00040.

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Sarwar, Badrul, George Karypis, Joseph Konstan, and John Reidl. "Item-based collaborative filtering recommendation algorithms." In the tenth international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/371920.372071.

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Wang, Jianmin, Raymond K. Wong, Jianwei Ding, Qinlong Guo, and Lijie Wen. "On Recommendation of Process Mining Algorithms." In 2012 IEEE 19th International Conference on Web Services (ICWS). IEEE, 2012. http://dx.doi.org/10.1109/icws.2012.52.

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Mazandarani, E., K. Yoshida, M. Koppen, and W. Bodrow. "Recommendation System Based on Competing Algorithms." In 2011 Third International Conference on Intelligent Networking and Collaborative Systems (INCoS). IEEE, 2011. http://dx.doi.org/10.1109/incos.2011.141.

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Sanchez-Vilas, Fernando, Jasur Ismoilov, Fabi´n P. Lousame, Eduardo Sanchez, and Manuel Lama. "Applying Multicriteria Algorithms to Restaurant Recommendation." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.124.

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Reports on the topic "Recommendation algorithms"

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Dang, Q. H. Recommendation for applications using approved hash algorithms. Gaithersburg, MD: National Institute of Standards and Technology, 2012. http://dx.doi.org/10.6028/nist.sp.800-107r1.

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Dang, Q. H. Recommendation for applications using approved hash algorithms. Gaithersburg, MD: National Institute of Standards and Technology, 2009. http://dx.doi.org/10.6028/nist.sp.800-107.

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Karypis, George. Evaluation of Item-Based Top-N Recommendation Algorithms. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada439546.

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Barker, Elaine B., and Allen L. Roginsky. Transitions: Recommendation for Transitioning the Use of Cryptographic Algorithms and Key Lengths. National Institute of Standards and Technology, November 2015. http://dx.doi.org/10.6028/nist.sp.800-131ar1.

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Cooper, David A., Daniel C. Apon, Quynh H. Dang, Michael S. Davidson, Morris J. Dworkin, and Carl A. Miller. Recommendation for Stateful Hash-Based Signature Schemes. National Institute of Standards and Technology, October 2020. http://dx.doi.org/10.6028/nist.sp.800-208.

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This recommendation specifies two algorithms that can be used to generate a digital signature, both of which are stateful hash-based signature schemes: the Leighton-Micali Signature (LMS) system and the eXtended Merkle Signature Scheme (XMSS), along with their multi-tree variants, the Hierarchical Signature System (HSS) and multi-tree XMSS (XMSSMT).
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Barker, W. C., and E. B. Barker. Recommendation for the triple data encryption algorithm (TDEA) block cipher. Gaithersburg, MD: National Institute of Standards and Technology, 2012. http://dx.doi.org/10.6028/nist.sp.800-67r1.

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Barker, Elaine, and Nicky Mouha. Recommendation for the Triple Data Encryption Algorithm (TDEA) block cipher. Gaithersburg, MD: National Institute of Standards and Technology, November 2017. http://dx.doi.org/10.6028/nist.sp.800-67r2.

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Barker, W. C. Recommendation for the triple data encryption algorithm (TDEA) block cipher. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.sp.800-67ver1.

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Barker, W. C. Recommendation for the triple data encryption algorithm (TDEA) block cipher. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.sp.800-67v1.

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Ayoul-Guilmard, Q., S. Ganesh, M. Nuñez, R. Tosi, F. Nobile, R. Rossi, and C. Soriano. D5.3 Report on theoretical work to allow the use of MLMC with adaptive mesh refinement. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.002.

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This documents describes several studies undertaken to assess the applicability of MultiLevel Monte Carlo (MLMC) methods to problems of interest; namely in turbulent fluid flow over civil engineering structures. Several numerical experiments are presented wherein the convergence of quantities of interest with mesh parameters are studied at different Reynolds’ numbers and geometries. It was found that MLMC methods could be used successfully for low Reynolds’ number flows when combined with appropriate Adaptive Mesh Refinement (AMR) strategies. However, the hypotheses for optimal MLMC performance were found to not be satisfied at higher turbulent Reynolds’ numbers despite the use of AMR strategies. Recommendations are made for future research directions based on these studies. A tentative outline for an MLMC algorithm with adapted meshes is made, as well as recommendations for alternatives to MLMC methods for cases where the underlying assumptions for optimal MLMC performance are not satisfied.
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