An open architecture for collaborative filtering of netnews

By Paul Resnick. 1994

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In Recommender Systems

Citations in the corpus

A Neural Autoregressive Approach to Collaborative Filtering (2016, background): “The explainatbility of the recommended results as well as the easy-to-implement nature of memory-based CF ensured its popularity in early recommender systems (Resnick et al., 1994).”
Explainable Recommendation: A Survey and New Perspectives (2018, background): “tering (Resnick et al. [1994]), explanations based on similar users (i.”
An MDP-Based Recommender System (2002, neutral): “Details of these and other methods are given in (e.g., Chen & Goodman, 1998).”
SoRec: social recommendation using probabilistic matrix factorization (2008, methodology): “User-based and item-based approaches often use the PCC algorithm [16] and the VSS algorithm [2]”
Content-based book recommending using learning for text categorization (1999, methodology): “Existing recommender systems almost exclusively utilize a form of computerized matchmaking called collaborative filtering or social filtering [1, 16, 39 , 27].”
Use of social network information to enhance collaborative filtering performance (2010, background): “This system predicts user preferences based on the opinions of other similar users who have rated the items according to preference (Herlocker et al., 2004; Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994).”
On the recommending of citations for research papers (2002, methodology): “User-Item CF User-Item CF is the original k-Nearest Neighbor CF algorithm [15].”
Learning Collaborative Information Filters (1998, methodology): “In order to avoid this problem, (Resnick et al., 1994) compute predictions according to the following formula, where U x is a rating to be predicted for User U on item x and r UJ is the correlation between users U and J. ∑ ∑ ∈ ∈ − + = x of Raters J UJ x of Raters J UJ x x r r J J U U ) ( where ∑ ∑…”
Evaluating collaborative filtering recommender systems (2004, unknown): “Much of the work in algorithm evaluation has focused on off-line analysis of predictive accuracy.”
Music recommendation and discovery in the long tail (2008, background): “Other early systems are: a music recommender named Ringo (Shardanand, 1994; Shardanand and Maes, 1995), and Group Lens, a system for rating USENET articles (Resnick et al., 1994).”
Personalized Machine Translation: Predicting Translational Preferences (2015, methodology): “Cosine similarity is a popular function for that purpose, and we also use it in our work (Resnick et al., 1994; Sarwar et al., 2001; Ricci et al., 2011).”
Collaborative Filtering with the Simple Bayesian Classifier (2000, methodology): “A variety of collaborative filtering algorithms have been reported and their performance has been evaluated empirically ([2], [15], [16]).”
Adaptive web search based on user profile constructed without any effort from users (2004, methodology): “The GroupLens research system [33, 24], which filters Usenet news, first introduced an automated collaborative filtering system using the k-nearest neighbor-based algorithm.”
A Framework for Collaborative, Content-Based and Demographic Filtering (1999, background): “Collaborative filtering (Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994; Shardanand & Maes, 1995) is an approach to making recommendations by finding correlations among users of a recommendation system.”