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
Citations
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Journal ArticleDOI
DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System
TL;DR: A general framework named DeepCF, short for Deep Collaborative Filtering, is proposed, to combine the strengths of the two types of methods and overcome such flaws in dot product and low-rank relations respectively.
Proceedings ArticleDOI
STAN: Spatio-Temporal Attention Network for Next Location Recommendation
TL;DR: Wang et al. as mentioned in this paper proposed a spatiotemporal attention network (STAN) for location recommendation, which explicitly exploits relative spatio-temporal information of all the check-ins with self-attention layers along the trajectory to aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations.
Journal ArticleDOI
An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems
TL;DR: This work proposes the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process, and is a promising model for implementing high-performance recommenders.
Proceedings ArticleDOI
Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering
TL;DR: This work proposes a novel algorithm, Tag-induced Cross-Domain Collaborative Filtering (TagCDCF), which exploits user-contributed tags that are common to multiple domains in order to establish the cross-domain links necessary for successful cross- domain CF.
Proceedings ArticleDOI
Wisdom of the better few: cold start recommendation via representative based rating elicitation
TL;DR: This paper proposes a principled approach to identify representative users and items using representative-based matrix factorization and shows that the selected representatives are superior to other competing methods in terms of achieving good balance between coverage and diversity.
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.