Proceedings ArticleDOI
Orthogonal graph-regularized matrix factorization and its application for recommendation
Zhenfeng Zhu,Peilu Xin,Shikui Wei,Yao Zhao +3 more
- pp 1-6
TLDR
An orthogonal matrix factorization model with graph regularization is proposed to preserve the consistency of the local structure both in user and item spaces, respectively and a novel dual-deflation technique is developed to incorporate into the sequential optimization.Abstract:
As one of the most successful approaches for recommendation, matrix factorization based Collaborative Filtering (CF) technique has received considerable attentions over the past years. In this paper, we propose an orthogonal matrix factorization model with graph regularization to preserve the consistency of the local structure both in user and item spaces, respectively. Instead of traditional alternating optimization method, a greedy sequential one is introduced to optimize a pair of coupled factor vector and its corresponding loading vector simultaneously each time, thus the original optimization problem is converted into the well-studied Multivariate Eigen Problem (MEP). Furthermore, multiple pairs of coupled eigen-vectors can be obtained in sequence. To guarantee nonrecurring of repetition of solutions, a novel dual-deflation technique is developed to incorporate into the sequential optimization. Experimental results on MovieLens and Each Movie data sets demonstrate that the proposed method is much more competitive compared with the state of the art matrix factorization based collaborative filtering methods.read more
Citations
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Journal ArticleDOI
Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction
TL;DR: An event-flow serializing method is developed to encode the evolution of dynamic heterogeneous graph into a special language pattern such as word sequence in a corpus and a dynamic attention-based graph embedding model is introduced to obtain an importance-awareness vectorized representation of the event flow.
Journal ArticleDOI
Matrix completion incorporating auxiliary information for recommender system design
Anupriya Gogna,Angshul Majumdar +1 more
TL;DR: Experimental evaluation indicates that the use of additional information indeed improves the accuracy of rating prediction, and a new formulation to incorporate this information into the matrix completion framework of latent factor based collaborative filtering is proposed.
Journal ArticleDOI
Leveraging Kernel-Incorporated Matrix Factorization for App Recommendation
TL;DR: This article proposes two kernel incorporated probabilistic matrix factorization models, which introduce app-categorical information to constrain the user and app latent features to be similar to their neighbors in the latent space, and proposes a novel kernelized non-negative Matrix factorization by incorporating non- negative constraints on latent factors to predict user-app preferences.
Proceedings ArticleDOI
Cross media topic analytics based on synergetic content and user behavior modeling
TL;DR: This paper proposes a solution framework for cross media topic analysis based on synergetic modeling of multi-modal content and user behavior, and proposes a multi-resolution user behavior modeling method to discover communities on the active Web users by considering the distribution of related atom topics along the temporal axis.
References
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Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: 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.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
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.
BookDOI
Recommender Systems Handbook
TL;DR: This handbook illustrates how recommender systems can support the user in decision-making, planning and purchasing processes, and works for well known corporations such as Amazon, Google, Microsoft and AT&T.