An automatic weighting scheme for collaborative filtering

By Rong Jin, Joyce Yue Chai, Luo Si. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004

373 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/8b0036371708ccd82add281511f10de416d7523b

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Citations in the corpus

Explainable recommendation with fusion of aspect information (2018, methodology): “Collaborative Filtering (CF) based approaches [4, 15, 34, 38] make rating prediction using a decomposition of a user-item rating matrix, and fall into two categories, the memory based CF [6, 11, 12] and the model based CF [16, 17, 31].”
Recommender Systems (2016, neutral): “Techniques for adjusting for the impact of very popular items are discussed in [98, 280].”
QoS-Aware Web Service Recommendation via Collaborative Filtering (2014, background): “The most analyzed examples of memory-based collaborative filtering include user-based approaches [3, 10, 14], item-based approaches [8, 17, 27], and their fusion [37, 31].”
Learning to recommend with social trust ensemble (2009, methodology): “These methods employ different strategies to find similar users and items for making the predictions, which are known as user-based approaches [3, 6, 9, 12] and item-based approaches [5, 10, 20], respectively.”
A Comprehensive Survey of Neighborhood-based Recommendation Methods (2011, methodology): “One of these strategies, described in [35], computes the factors λi by maximizing the average similarity between users.”
Social Recommendation Using Low-Rank Semidefinite Program (2011, methodology): “For the neighborhood-based methods (Linden, Smith, and York 2003; Breese, Heckerman, and Kadie 1998; Jin, Chai, and Si 2004), they mainly try to search for the similar active users or items in order to predict the ratings.”
QoS Ranking Prediction for Cloud Services (2013, background): “The most analyzed examples of memory-based collaborative filtering include user-based approaches [4], [9], item-based approaches [7], [11], [16], and their fusion [13], [16], [17], [22], [23].”
SoRec: social recommendation using probabilistic matrix factorization (2008, methodology): “The most analyzed examples of memory-based collaborative filtering include userbased approaches [2, 7, 10, 25] and item-based approaches [4, 12, 19].”
Effective missing data prediction for collaborative filtering (2007, methodology): “In this algorithm, we set the similarity threshold for users and items respectively, and the prediction algorithm will determine whether predicting the missing data or not.”
Recommender systems with social regularization (2011, methodology): “Neighborhoodbased methods mainly focus on finding the similar users [4, 12] or items [6, 17, 32] for recommendations.”
Unifying user-based and item-based collaborative filtering approaches by similarity fusion (2006, methodology): “Examples of memory-based collaborative ﬁltering include user-based methods [1, 5, 9, 14] and item-based meth-ods [ , 15].”
Probabilistic factor models for web site recommendation (2011, methodology): “Neighborhood-based methods, or memory-based methods, mainly concentrate on finding the similar users [2, 10] or items [5, 17, 24] for generating recommendations.”
WSRec: A Collaborative Filtering Based Web Service Recommender System (2009, background): “The most analyzed examples of memory-based collaborative filtering include user-based approaches [2, 7] and item-based approaches [5, 13].”