Matrix Factorization Techniques for Recommender Systems

By Yehuda Koren, Robert M. Bell, C. Volinsky. 2009

10,960 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Marketing, Information Retrieval & Search

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Citations in the corpus

On Sampling Strategies for Neural Network-based Collaborative Filtering (2017, methodology): “In most of existing matrix factorization techniques [17, 18, 27], each user/item ID is associated with a latent vector u or v (i.”
Image-Based Recommendations on Styles and Substitutes (2015, background): “The system shares these characteristics with more common forms of recommender system, but does so on the basis of the appearance of the object, rather than metadata, reviews, or similar.”
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, methodology): “Traditional recommender systems treat each user’s review history as a series of unordered events, which are simply used to build a model for that user, such as a latent factor model [22].”
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (2012, methodology): “These model-based approaches, especially latent-factor models based on matrix factorization (Koren et al., 2009; Salakhutdinov & Mnih, 2008), have shown promise in better rating prediction since they eﬃciently incorporate user interests into the model.”
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks (2017, methodology): “The first type of methods includes factor models that learn a low-rank decomposition of sparse user-item interactions matrix [3, 12, 21].”
Improved Recurrent Neural Networks for Session-based Recommendations (2016, methodology): “Matrix factorization methods [15, 30] are based on the sparse user-item interaction matrix, where the recommendation problem is formulated as a matrix completion task.”
A Neural Autoregressive Approach to Collaborative Filtering (2016, background): “Bias MF (Koren et al., 2009) is proposed to improve the performance of SVD by introducing systematic biases associated with users and items.”
On the Effectiveness of Linear Models for One-Class Collaborative Filtering (2016, methodology): “The basic idea is to embed users and items into some shared latent space (Srebro and Jaakkola 2003; Koren, Bell, and Volinsky 2009).”
Session-based Recommendations with Recurrent Neural Networks (2015, neutral): “The most common methods used in recommender systems are factor models (Koren et al., 2009; Weimer et al., 2007; Hidasi & Tikk, 2012) and neighborhood methods (Sarwar et al., 2001; Ko-ren, 2008).”
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, background): “ecommendation strategies, collaborative filtering(CF), which considers users’ historical interactions and makes recommendations based on their potential common preferences, has achieved great success [12]. However, CF-based methods usually suffer from the sparsity of user-item interactions and the c”
Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, methodology): “• Basic-MF [13] simply uses matrix factorization method to predict the user-voting matrix while ignores additional social relation, group affiliation and voting content information.”
Poisoning Attacks to Graph-Based Recommender Systems (2018, methodology): “d 4000 times in the white-box se−ing and black-box se−ing, respectively, when the a−ack size is 1%. „is is because matrix factorization is known to achieve be−er hit ratios when the dataset is denser [17]. For instance, matrix factorization achieves lower hit ratios than the graph-based recommender ”
Explainable Recommendation: A Survey and New Perspectives (2018, background): “Among the many LFMs, Matrix Factorization (MF) and its variants were especially successful in rating prediction tasks (Koren et al., 2009).”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “Of the latent factor methods, matrix factorization is a successful and popular approach [36].”
Why Amazon's Ratings Might Mislead You: The Story of Herding Effects (2014, neutral): “For example, the product’s perception and popularity might change as new selections emerge; customers’ inclinations might evolve over time 10,11 ; early and late adopters of the product might have different rating tendencies.”
Counterfactual inference for consumer choice across many product categories (2019, background): “systems literature that uses matrix-factorizationbased techniques to predict what items (movies, links, articles, search results, etc.) that a user will enjoy based on their previous choice behavior (Koren et al., 2009; Bobadilla et al., 2013). A core insight of this literature is that it is often v”
Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, methodology): “From item-based collaborative filtering [33] to matrix factorization techniques [21], various”