.png){kind=icon}
Return
MULTILAYER TENSOR FACTORIZATION WITH APPLICATIONS TO RECOMMENDER SYSTEMS
.png){kind=icon}
OA
AI
DOI：10.1214/17-AOS1659.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Cold-start problem
context-aware recommender system
maximum block improvement
nonconvex optimization
tensor completion
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.7
Papers:
2.8K
Citations:
2.9W
Organization
Cited Papers
Prevalent and persistent viral infection in cultures of the coral algal endosymbiont Symbiodinium.png)
Coral Reefs
Coral ReefsIF0
PREAI
ShareSave
ShareSaveShareSaveMULTILAYER TENSOR FACTORIZATION WITH APPLICATIONS TO RECOMMENDER SYSTEMSANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveTuning the water permeability of ultra-high molecular weight polyethylene microporous membrane by molecular self-assembly and flow fieldPolymer
PolymerIF0
PREAI
ShareSaveShareSave
View more