Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account. By introducing additional factors for time, we formalize this problem as a tensor factorization with a special constraint on the time dimension. Further, we provide a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control. To learn the model we develop an efficient sampling procedure that is capable of analyzing large-scale data sets. This new algorithm, called Bayesian Probabilistic Tensor Factorization (BPTF), is evaluated on several real-world problems including sales prediction and movie recommendation. Empirical results demonstrate the superiority of our temporal model.
Code (0)
등록된 구현이 없습니다.
Tasks
Collaborative FilteringMovie RecommendationRecommendation SystemsSimilar Papers 제목 키워드 기반
Convergence rate of Bayesian tensor estimator: Optimal rate without restricted strong convexity
In this paper, we investigate the statistical convergence rate of a Bayesian low-rank tensor estimator. Our problem setting is the regression problem where a tensor structure underlying the data is estimated. This proble…
Collaborative FilteringMulti-Task LearningregressionCoupled Variational Recurrent Collaborative Filtering
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstr…
Collaborative FilteringRecommendation SystemsVariational InferenceA Novel Deterministic Framework for Non-probabilistic Recommender Systems
Recommendation is a technique which helps and suggests a user, any relevant item from a large information space. Current techniques for this purpose include non-probabilistic methods like content-based filtering an…
Bayesian InferenceCollaborative FilteringRecommendation SystemsVariational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still la…
Bayesian InferenceCollaborative FilteringLanguage ModelingLanguage Modelling+2Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (vaes) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still la…
Bayesian InferenceCollaborative FilteringLanguage ModelingLanguage Modelling+2