The Annals of Statistics

Multilayer tensor factorization with applications to recommender systems

Xuan Bi, Annie Qu, and Xiaotong Shen

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Abstract

Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence. In this article, we propose an innovative method, namely the recommendation engine of multilayers (REM), for tensor recommender systems. The proposed method utilizes the structure of a tensor response to integrate information from multiple modes, and creates an additional layer of nested latent factors to accommodate between-subjects dependency. One major advantage is that the proposed method is able to address the “cold-start” issue in the absence of information from new customers, new products or new contexts. Specifically, it provides more effective recommendations through sub-group information. To achieve scalable computation, we develop a new algorithm for the proposed method, which incorporates a maximum block improvement strategy into the cyclic blockwise-coordinate-descent algorithm. In theory, we investigate algorithmic properties for convergence from an arbitrary initial point and local convergence, along with the asymptotic consistency of estimated parameters. Finally, the proposed method is applied in simulations and IRI marketing data with 116 million observations of product sales. Numerical studies demonstrate that the proposed method outperforms existing competitors in the literature.

Article information

Source
Ann. Statist., Volume 46, Number 6B (2018), 3308-3333.

Dates
Received: July 2017
Revised: September 2017
First available in Project Euclid: 11 September 2018

Permanent link to this document
https://projecteuclid.org/euclid.aos/1536631275

Digital Object Identifier
doi:10.1214/17-AOS1659

Mathematical Reviews number (MathSciNet)
MR3852653

Zentralblatt MATH identifier
06965689

Subjects
Primary: 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11]
Secondary: 90C26: Nonconvex programming, global optimization 68T05: Learning and adaptive systems [See also 68Q32, 91E40]

Keywords
Cold-start problem context-aware recommender system maximum block improvement nonconvex optimization tensor completion

Citation

Bi, Xuan; Qu, Annie; Shen, Xiaotong. Multilayer tensor factorization with applications to recommender systems. Ann. Statist. 46 (2018), no. 6B, 3308--3333. doi:10.1214/17-AOS1659. https://projecteuclid.org/euclid.aos/1536631275


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Supplemental materials

  • Supplement to “Multilayer tensor factorization with applications to recommender systems.”. Technical proof of all lemmas, propositions and theorems are provided in the supplementary material [5].