Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
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TLDR
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.Abstract:
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levelsread more
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Proceedings ArticleDOI
Towards Long-term Fairness in Recommendation
Yingqiang Ge,Shuchang Liu,Ruoyuan Gao,Yikun Xian,Yunqi Li,Xiangyu Zhao,Changhua Pei,Fei Sun,Junfeng Ge,Wenwu Ou,Yongfeng Zhang +10 more
TL;DR: In this paper, the authors propose a fairness-constrained reinforcement learning algorithm for recommendation, which models the recommendation problem as a Constrained Markov Decision Process (CMDP), so that the model can dynamically adjust its recommendation policy to make sure the fairness requirement is always satisfied when the environment changes.
Proceedings ArticleDOI
Low-tubal-rank Tensor Completion using Alternating Minimization
TL;DR: In this article, a fast iterative algorithm, called "Tubal-Alt-Min", was proposed for low-tubal-rank tensor completion by observing a subset of its elements selected uniformly at random.
Proceedings ArticleDOI
App recommendation: a contest between satisfaction and temptation
TL;DR: This work proposes an Actual- Tempting model that captures factors that invoke a user to replace an old app with a new app and shows that the AT model performs significantly better than the conventional recommendation techniques such as collaborative filtering and content-based recommendation.
Journal ArticleDOI
Reversed CF
TL;DR: Reversed CF (RCF), a rapid CF algorithm which utilizes a k-nearest neighbor (k-NN) graph, which outperforms traditional user-based/item-based collaborative filtering algorithms in terms of both preprocessing time and query processing time without sacrificing the level of accuracy.
Proceedings ArticleDOI
Recommending Web Services via Combining Collaborative Filtering with Content-Based Features
TL;DR: This paper proposes a novel approach that dynamically recommends Web services that fit users' interests that combines collaborative filtering and content-based recommendation using a three-way aspect model.
References
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Journal ArticleDOI
Using collaborative filtering to weave an information tapestry
TL;DR: Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser.
Proceedings Article
Probabilistic Matrix Factorization
Andriy Mnih,Ruslan Salakhutdinov +1 more
TL;DR: The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.
Proceedings ArticleDOI
Factorization meets the neighborhood: a multifaceted collaborative filtering model
TL;DR: The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model and a new evaluation metric is suggested, which highlights the differences among methods, based on their performance at a top-K recommendation task.
Proceedings ArticleDOI
Collaborative Filtering for Implicit Feedback Datasets
TL;DR: This work identifies unique properties of implicit feedback datasets and proposes treating the data as indication of positive and negative preference associated with vastly varying confidence levels, which leads to a factor model which is especially tailored for implicit feedback recommenders.
The Netflix Prize
James Bennett,Stan Lanning +1 more
TL;DR: Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining, machine learning and computer science communities to develop systems that could beat the accuracy of its recommendation system, Cinematch.