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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Proceedings ArticleDOI
Comparative Deep Learning of Hybrid Representations for Image Recommendations
TL;DR: Zhang et al. as discussed by the authors designed a dual-net deep network, in which the two sub-networks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions.
Journal ArticleDOI
User trends modeling for a content-based recommender system
TL;DR: The concept of trend to capture the interests of user in selecting items among different group of similar items is introduced and the trend based user model is constructed by incorporating user profile into a new extension of Distance Dependent Chines Restaurant Process (dd-CRP).
Proceedings ArticleDOI
Neural Tensor Factorization for Temporal Interaction Learning
TL;DR: A Neural network based Tensor Factorization (NTF) model for predictive tasks on dynamic relational data that incorporates the multi-layer perceptron structure for learning the non-linearities between different latent factors.
Proceedings ArticleDOI
Model recommendation: Generating object detectors from few samples
Yu-Xiong Wang,Martial Hebert +1 more
TL;DR: An approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples, which will make the models informative across different categories, and enable rapid generation of new detectors.
Journal ArticleDOI
Relational Collaborative Topic Regression for Recommender Systems
TL;DR: A novel hierarchical Bayesian model called Relational Collaborative Topic Regression (RCTR) is developed, which extends CTR by seamlessly integrating the user-item feedback information, item content information, and network structure among items into the same 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.