MovieLens unplugged: experiences with an occasionally connected recommender system

By Bradley N. Miller, I. Albert, Shyong K. Lam, J. Konstan, John Riedl. 2003

613 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems

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Citations in the corpus

How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “For example, MovieLens is a research website that uses collaborative ltering to make movie recommendations [45]; the researchers behind it have released several public datasets6 that are used prolically in evaluation recommendation systems [23].”
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017, methodology): “For the MovieLens dataset we constructed the user and item (movie) graphs as unweighted 10-nearest neighbor graphs in the space of user and movie features, respectively.”
A Review of Content-Based and Context-Based Recommendation Systems (2021, methodology): “Some recommender frameworks, for example, Movielens [13], depend on collaborative filtering to customize the proposal of things.”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “…applications, including the ones for recommending books, CDs, and news articles [64], [88], and some of these methods are used in the “industrial-strength” recommender systems, such as the ones deployed at Amazon [61], MovieLens [67], and VERSIFI Technologies (formerly AdapiveInfo.com) [14].”
Explaining Recommendations: Satisfaction vs. Promotion (2005, methodology): “Some were developed for research purposes such as GroupLens [11], Video Recommender [6], Ringo [13], and MovieLens on a PDA [8], and some are in commercial use such as Amazon , NetFlix, CDNow, and MovieFinder.”
Performance of recommender algorithms on top-n recommendation tasks (2010, methodology): “RESULTS In this section wepresent the quality of the recommender algorithmspresented inSection3 ontwostandarddatasets: MovieLens [13] and Net.ix [3].”
A Comprehensive Survey of Neighborhood-based Recommendation Methods (2011, background): “recommendation of books and CDs [47, 53], music [45, 70], movies [31, 51, 5], news [6, 41, 76], jokes [23], and web pages [7, 52].”
Dual-Primal Graph Convolutional Networks (2018, methodology): “We repeated verbatim the experiment of [34, 27] on several standard datasets used in the recommender systems literature (MovieLens [32], Flixster [23], Douban [30], and YahooMusic [11]), using different convolutional layers inside RMGCNN (Chebyshev [10], Cayley [27], GAT [42] and the proposed…”
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters (2017, methodology): “Here, we repeated verbatim their experiment on the MovieLens dataset ([34]), replacing Chebyshev filters with Cayley filters.”
Strathprints Institutional Repository Bayesian Latent Variable Models for Collaborative Item Rating Prediction. In: Acm Cikm, 2011-03-31. Bayesian Latent Variable Models for Collaborative Item Rating Prediction (background): “The maximum number of ratings for any user is 2876, 97% of users have fewer than 500 ratings and the minimum is 10 (this lower limit is imposed by the MovieLens web site).”
Evaluating collaborative filtering recommender systems (2004, methodology): “A film recommender that uses zip code to narrow the theater search, such as Miller et al.’s MovieLens Unplugged [Miller et al. 2003], seems more likely to provide meaningful data.”