Dynamic tensor recommender systems

Yanqing Zhang, Xuan Bi, Niansheng Tang, Annie Qu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying coefficient model for temporal tensor factorization through a polynomial spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the spline coefficient estimator. The proposed method is applied to simulations, IRI marketing data and Last.fm data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting.

Original languageEnglish (US)
JournalJournal of Machine Learning Research
StatePublished - 2021

Bibliographical note

Funding Information:
We would like to thank the action editors and referees for insightful comments and suggestions which improve the article significantly. We would like to acknowledge support for this project from the National Science Foundation Grants (DMS1821198, DMS1613190 and DMS1952402), National Natural Science Foundation of P.R. China (11731011, 11671349 and 12001479), and Natural Science Foundation of Yunnan Province of China (2019FD068). We would like to thank IRI for making the data available. All estimates and analysis in this paper based on data provided by IRI are by the authors and not by the IRI.

Publisher Copyright:
© 2021 Yanqing Zhang, Xuan Bi, Niansheng Tang and Annie Qu.


  • Contextual information
  • Dynamic recommender systems
  • Polynomial spline approximation
  • Prediction interval
  • Product sales forecasting


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