Relevant bibliographies by topics / COLLABORATIVE FILTERING ALGORITHMS

Contents

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

Academic literature on the topic 'COLLABORATIVE FILTERING ALGORITHMS'

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Published: 26 October 2023

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Journal articles on the topic "COLLABORATIVE FILTERING ALGORITHMS"

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Ben Kharrat, Firas, Aymen Elkhleifi, and Rim Faiz. "Improving Collaborative Filtering Algorithms." International Journal of Knowledge Society Research 7, no. 3 (July 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 (February 2011): 1–33. http://dx.doi.org/10.1145/1921591.1921593.

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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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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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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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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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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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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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Kourtiche, Ali, and Mohamed Merabet. "Collaborative Filtering Technical Comparison in Implicit Data." International Journal of Knowledge-Based Organizations 11, no. 4 (October 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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Dissertations / Theses on the topic "COLLABORATIVE FILTERING ALGORITHMS"

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Hansjons, Vegeborn Victor, and Hakim Rahmani. "Comparison and Improvement Of Collaborative Filtering Algorithms." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209468.

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Recommender Systems is a topic several computer scientists have researched. With today’s e-commerce and Internet access, companies try to maximize their profit by utilizing var- ious recommender algorithms. One methodology used in such systems is Collaborative Filtering. The objective of this paper is to compare four algorithms, all based on Collaborative Filtering, which are k-Nearest-Neighbour, Slope One, Singular Value Decomposition and Average Least Square algorithms, in order to find out which algorithm produce the best pre- diction rates. In addition, the paper will also use two mathematical models, the Arithmetic Median and Weighted Arithmetic Mean, to determine if they can improve the prediction rates. Singular Value Decomposition performed the best out of the four algorithms and Aver- age Least Square performed the worst. However, the Arithmetic Median performed slightly better than Singular Value Decomposition and the Weighted Arithmetic Mean performed the worst.
Rekommendationssystem är ett ämne som många datatekniker har forskat inom. Med dagens e-handel och Internetåtkomst, så försöker företag att maximera sina vinster genom att utnyttja diverse rekommendationsalgoritmer. En metodik som används i sådana system är Collaborative Filtering. Syftet med denna uppsats är att jämföra fyra algoritmer, alla baserade på Collaborati- ve Filtering, vilket är k-Nearest-Neighbour, Slope One, Single Value Decomposition och Average Least Square, i syfte att ta reda på vilken algoritm som producerar den bästa be- tygsättningen. Uppsatsen kommer även använda sig av två olika matematiska modeller, Aritmetisk Median och Viktad Aritmetisk Median, för att ta reda på om dom kan förbättra betygsättningen. Single Value Decomposition presterade bäst medan Average Least Square presterade sämst av de fyra algoritmerna. Däremot presterade Aritmetiska Median en aning bättre än Single Value Decomposition och Viktad Aritmetisk Median presterade sämst.
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Anne, Patricia Anne. "Semantically and Contextually-Enhanced Collaborative Filtering Recommender Algorithms." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516289.

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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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Rault, Antoine. "User privacy in collaborative filtering systems." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S019/document.

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Les systèmes de recommandation essayent de déduire les intérêts de leurs utilisateurs afin de leurs suggérer des items pertinents. Ces systèmes offrent ainsi aux utilisateurs un service utile car ils filtrent automatiquement les informations non-pertinentes, ce qui évite le problème de surcharge d’information qui est courant de nos jours. C’est pourquoi les systèmes de recommandation sont aujourd’hui populaires, si ce n’est omniprésents dans certains domaines tels que le World Wide Web. Cependant, les intérêts d’un individu sont des données personnelles et privées, comme par exemple son orientation politique ou religieuse. Les systèmes de recommandation recueillent donc des données privées et leur utilisation répandue nécessite des mécanismes de protection de la vie privée. Dans cette thèse, nous étudions la protection de la confidentialité des intérêts des utilisateurs des systèmes de recommandation appelés systèmes de filtrage collaboratif (FC). Notre première contribution est Hide & Share, un nouveau mécanisme de similarité, respectueux de la vie privée, pour la calcul décentralisé de graphes de K-Plus-Proches-Voisins (KPPV). C’est un mécanisme léger, conçu pour les systèmes de FC fondés sur les utilisateurs et décentralisés (ou pair-à-pair), qui se basent sur les graphes de KPPV pour fournir des recommandations. Notre seconde contribution s’applique aussi aux systèmes de FC fondés sur les utilisateurs, mais est indépendante de leur architecture. Cette contribution est double : nous évaluons d’abord l’impact d’une attaque active dite « Sybil » sur la confidentialité du profil d’intérêts d’un utilisateur cible, puis nous proposons une contre-mesure. Celle-ci est 2-step, une nouvelle mesure de similarité qui combine une bonne précision, permettant ensuite de faire de bonnes recommandations, avec une bonne résistance à l’attaque Sybil en question
Recommendation systems try to infer their users’ interests in order to suggest items relevant to them. These systems thus offer a valuable service to users in that they automatically filter non-relevant information, which avoids the nowadays common issue of information overload. This is why recommendation systems are now popular, if not pervasive in some domains such as the World Wide Web. However, an individual’s interests are personal and private data, such as one’s political or religious orientation. Therefore, recommendation systems gather private data and their widespread use calls for privacy-preserving mechanisms. In this thesis, we study the privacy of users’ interests in the family of recommendation systems called Collaborative Filtering (CF) ones. Our first contribution is Hide & Share, a novel privacy-preserving similarity mechanism for the decentralized computation of K-Nearest-Neighbor (KNN) graphs. It is a lightweight mechanism designed for decentralized (a.k.a. peer-to-peer) user-based CF systems, which rely on KNN graphs to provide recommendations. Our second contribution also applies to user-based CF systems, though it is independent of their architecture. This contribution is two-fold: first we evaluate the impact of an active Sybil attack on the privacy of a target user’s profile of interests, and second we propose a counter-measure. This counter-measure is 2-step, a novel similarity metric combining a good precision, in turn allowing for good recommendations,with high resilience to said Sybil attack
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Strunjas, Svetlana. "Algorithms and Models for Collaborative Filtering from Large Information Corpora." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182.

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Almosallam, Ibrahim Ahmad Shang Yi. "A new adaptive framework for collaborative filtering prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5630.

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Thesis (M.S.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 22, 2008) Includes bibliographical references.
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Salam, Patrous Ziad, and Safir Najafi. "Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186456.

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Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two specific recommender algorithms, namely FunkSVD, a matrix factorization algorithm and Item-based collaborative filtering, which utilizes item similarity. This study aims to compare the prediction accuracy of the algorithms when ran on a small and a large dataset. By performing cross-validation on the algorithms, this paper seeks to obtain data that supposedly may clarify ambiguities regarding the accuracy of the algorithms. The tests yielded results which indicated that the FunkSVD algorithm may be more accurate than the Item-based collaborative filtering algorithm, but further research is required to come to a concrete conclusion.
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NARAYANASWAMY, SHRIRAM. "A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

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Svebrant, Henrik, and John Svanberg. "A comparative study of the conventional item-based collaborative filtering and the Slope One algorithms for recommender systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186449.

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Recommender systems are an important research topic in todays society as the amount of data increases across the globe. In order for commercial systems to give their users good and personalized recommendations on what data may be of interest to them in an effective manner, such a system must be able to give recommendations quickly and scale well as data increases. The purpose of this study is to evaluate two such algorithms with this in mind.  The two different algorithm families tested are classified as item-based collaborative filtering but work very differently. It is therefore of interest to see how their complexities affect their performance, accuracy as well as scalability. The Slope One family is much simpler to implement and proves to be equally as efficient, if not even more efficient than the conventional item-based ones. Both families do require a precomputation stage before recommendations are possible to give, this is the stage where Slope One suffers in comparison to the conventional item-based one. The algorithms are tested using Lenskit, on data provided by GroupLens and their MovieLens project.
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Safran, Mejdl Sultan. "EFFICIENT LEARNING-BASED RECOMMENDATION ALGORITHMS FOR TOP-N TASKS AND TOP-N WORKERS IN LARGE-SCALE CROWDSOURCING SYSTEMS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1511.

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A pressing need for efficient personalized recommendations has emerged in crowdsourcing systems. On the one hand, workers confront a flood of tasks, and they often spend too much time to find tasks matching their skills and interests. Thus, workers want effective recommendation of the most suitable tasks with regard to their skills and preferences. On the other hand, requesters sometimes receive results in low-quality completion since a less qualified worker may start working on a task before a better-skilled worker may get hands on. Thus, requesters want reliable recommendation of the best workers for their tasks in terms of workers' qualifications and accountability. The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers' capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches (mostly developed for e-commerce markets) no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this research, we reveal our insight into the essential difference between the tasks in crowdsourcing systems and the products/items in e-commerce markets, and the difference between buyers' interests in products/items and workers' interests in tasks. Our insight inspires us to bring up categories as a key mediation mechanism between workers and tasks. We propose a two-tier data representation scheme (defining a worker-category suitability score and a worker-task attractiveness score) to support personalized task and worker recommendation. We also extend two optimization methods, namely least mean square error (LMS) and Bayesian personalized rank (BPR) in order to better fit the characteristics of task/worker recommendation in crowdsourcing systems. We then integrate the proposed representation scheme and the extended optimization methods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top-N recommendation algorithms for crowdsourcing systems: (1) Top-N-Tasks (TNT) recommendation algorithms for discovering the top-N most suitable tasks for a given worker, and (2) Top-N-Workers (TNW) recommendation algorithms for identifying the top-N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.
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Books on the topic "COLLABORATIVE FILTERING ALGORITHMS"

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Nadler, Anthony M. Popularizing News 2.0. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040146.003.0005.

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This chapter examines attempts to popularize and democratize news online through collaborative filtering. Collaborative filtering offers a means to replace the role of professional editors in setting the news agenda and deciding which stories deserve the most prominence. Instead of professional editors, collaborative filtering relies on algorithms to sort, rank, and prioritize the news based on the activity of large groups of web users. Various news sites have added some aspect of collaborative filtering, but the chapter focuses on social news sites (Reddit, Newsvine, and Slashdot) because they allow their users to make conscious voting choices about which stories should be most prominent. These sites epitomize the defining characteristics of the social Web and apply them to news.
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Book chapters on the topic "COLLABORATIVE FILTERING ALGORITHMS"

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Nisgav, Aviv, and Boaz Patt-Shamir. "Improved Collaborative Filtering." In Algorithms and Computation, 425–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25591-5_44.

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Chang, Edward Y. "Parallel Algorithms for Collaborative Filtering." In Algorithmic Aspects in Information and Management, 2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02158-9_2.

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Cunha, Tiago, Carlos Soares, and André C. P. L. F. de Carvalho. "Selecting Collaborative Filtering Algorithms Using Metalearning." In Machine Learning and Knowledge Discovery in Databases, 393–409. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46227-1_25.

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Kluver, Daniel, Michael D. Ekstrand, and Joseph A. Konstan. "Rating-Based Collaborative Filtering: Algorithms and Evaluation." In Social Information Access, 344–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90092-6_10.

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Pan, Lilin, and Jianfei Shao. "Review of Improved Collaborative Filtering Recommendation Algorithms." In Advances in Intelligent Systems and Computing, 21–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1843-7_3.

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Cunha, Tiago, Carlos Soares, and André C. P. L. F. de Carvalho. "Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers." In Discovery Science, 189–203. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67786-6_14.

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Cai, Xianggao, Zhanpeng Xu, Guoming Lai, Chengwei Wu, and Xiaola Lin. "GPU-Accelerated Restricted Boltzmann Machine for Collaborative Filtering." In Algorithms and Architectures for Parallel Processing, 303–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33078-0_22.

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Verhaegh, Wim F. J., Aukje E. M. van Duijnhoven, Pim Tuyls, and Jan Korst. "Privacy Protection in Collaborative Filtering by Encrypted Computation." In Intelligent Algorithms in Ambient and Biomedical Computing, 169–84. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-4995-1_11.

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Adán-Coello, Juan Manuel, and Carlos Miguel Tobar. "Using Collaborative Filtering Algorithms for Predicting Student Performance." In Electronic Government and the Information Systems Perspective, 206–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44159-7_15.

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Papagelis, Manos, Ioannis Rousidis, Dimitris Plexousakis, and Elias Theoharopoulos. "Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms." In Lecture Notes in Computer Science, 553–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11425274_57.

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Conference papers on the topic "COLLABORATIVE FILTERING ALGORITHMS"

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Kharrat, Firas Ben, Aymen Elkhleifi, and Rim Faiz. "Improving Collaborative Filtering Algorithms." In 2016 12th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2016. http://dx.doi.org/10.1109/skg.2016.024.

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Yaqiu Liu, Zhendi Wang, and Man Li. "Ratio-based collaborative filtering algorithms." In 2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics (ISSCAA). IEEE, 2008. http://dx.doi.org/10.1109/isscaa.2008.4776258.

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Kleinberg, Jon, and Mark Sandler. "Convergent algorithms for collaborative filtering." In the 4th ACM conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/779928.779929.

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Patil, Vandana A., and Lata Ragha. "Comparing performance of collaborative filtering algorithms." In 2012 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE, 2012. http://dx.doi.org/10.1109/iccict.2012.6398206.

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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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"Comparative Study of Collaborative Filtering Algorithms." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004104001320137.

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Matuszyk, Pawel, and Myra Spiliopoulou. "Predicting the Performance of Collaborative Filtering Algorithms." In the 4th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2611040.2611054.

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8

"Using Collaborative Filtering Algorithms as eLearning Tools." In 2009 42nd Hawaii International Conference on System Sciences. IEEE, 2009. http://dx.doi.org/10.1109/hicss.2009.492.

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9

Liu, Dong. "A Study on Collaborative Filtering Recommendation Algorithms." In 2018 IEEE 4th International Conference on Computer and Communications (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/compcomm.2018.8780979.

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10

Cöster, Rickard, and Martin Svensson. "Inverted file search algorithms for collaborative filtering." In the 25th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/564376.564420.

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