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Matrix Factorization Techniques for Recommender Systems
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
Factorization meets the neighborhood: a multifaceted collaborative filtering model
  • Y. Koren
  • Computer Science
    KDD
  • 24 August 2008
TLDR
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.
Collaborative Filtering for Implicit Feedback Datasets
TLDR
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.
Collaborative filtering with temporal dynamics
TLDR
Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns.
Performance of recommender algorithms on top-n recommendation tasks
TLDR
An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task, and new variants of two collaborative filtering algorithms are offered.
Advances in Collaborative Filtering
The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has
Factor in the neighbors: Scalable and accurate collaborative filtering
TLDR
A new neighborhood model with an improved prediction accuracy is introduced, which model neighborhood relations by minimizing a global cost function and makes both item-item and user-user implementations scale linearly with the size of the data.
Lessons from the Netflix prize challenge
This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
TLDR
This work enhances the neighborhood-based approach leading to substantial improvement of prediction accuracy, without a meaningful increase in running time, and suggests a novel scheme for low dimensional embedding of the users.
Graph Drawing by Stress Majorization
TLDR
This work shows how to draw graphs by stress majorization, adapting a technique known in the MDS community for more than two decades and appears that majorization has advantages over the technique of Kamada and Kawai in running time and stability.
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