GroupLens: applying collaborative filtering to Usenet news

By J. Konstan, Bradley N. Miller, David A. Maltz, Jonathan L. Herlocker, Lee R. Gordon, John Riedl. 1997

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Citations in the corpus

Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, methodology): “CF relies on factorizing user-item interaction matrices to generate latent factors representing users and items [16, 17, 25, 30].”
Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval (2008, background): “However, since such measures have been found to be inaccurate as indicators of the user interest [6], we did not consider the URL weight in the present work.”
Seventh IEEE International Conference on Data Mining Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights (background): “This has led to many papers (e.g., [8]), research projects (e.g., [9]) and commercial systems (e.g., [11]) based on CF.”
Recommendation Systems in Software Engineering (2014, methodology): “This seamless information includes navigation and browsing aids such as: text links [18, 20]; clickable links as in FrUiT, Hipikat, Strathcona, and CodeBroker; annotations or highlights of relevant parts of the source code as in Strathcona and CodeBroker; or graphical notations as in Strathcona, for”
Explaining collaborative filtering recommendations (2000, methodology): “The user may not even be aware that the ACF system is collecting implicit ratings, such as time-spentreading[ 9 ] to fuel the recommendations.”
Evaluating collaborative filtering recommender systems (2004, methodology): “In our own early work designing recommender algorithms for Usenet News [Konstan et al. 1997, Miller et al. 1997], we experimented with a variety of synthesized datasets.”
A recommender system using GA K-means clustering in an online shopping market (2008, background): “Moreover, it can recommend items in novel territories because it determines items to be recommended by considering only similarity between users, not characteristics of products (Konstan et al., 1997; Pazzani, 1999).”
Expertise recommender: a flexible recommendation system and architecture (2000, background): “Recommendation systems have been used to recommend a variety of things including Usenet news [ 11 ], web pages [2, 9], videos [8], movies, music [18] and books.”
Social dynamics of Digg (2010, background): “Moreover, this information could be useful to recommend new fans to users, based on visibility-adjusted similarity in voting rather than, as commonly done in collaborative filtering [27], just using the raw score of similar votes.”
Theoretical Analysis of Bayesian Matrix Factorization (2011, methodology): “Collaborative filtering (Konstan et al., 1997; Baldi and Brunak, 1998; Funk, 2006) and image processing (Lee and Seung, 1999) would be popular applications of MF. Note that, some of these applications such as collaborative filtering and multi-task learning with unshared input sets are out of scope…”
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, methodology): “It is from the MovieLens system [41] maintained by the GroupLens research team. b) D2: Extended Epinion.”
Global analytic solution of fully-observed variational Bayesian matrix factorization (2013, methodology): “This excludes the collaborative filtering setup, which is aimed at imputing missing entries of an observed matrix (Konstan et al., 1997; Funk, 2006).”