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Activity Number: 68 - Modern Statistical Learning Methods
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313759
Title: Multilayer Recommender Systems Using with Dependent via Tensor
Author(s): Jiuchen Zhang* and Annie Qu and Yubai Yuan
Companies: Univ of California in Irvine and University of California Irvine and University of Illinois at Urbana-Champaign
Keywords: dependent modeling; multi-source data integration; tensor decomposition with dependency

In this work, we propose a novel multilayer recommender system to integrate multi-source multilayer data. Specifically, we utilize tensor structure to combine multilayer information embedded by latent features shared by grouping features arising from users, items and locations. To incorporate the dependency among latent features, we introduce dependent tensor decomposition based on multilayer features from multi-source data. One major advantage is that the proposed method is able to simultaneously capture the complex dependency structure among latent features across different data sources, hence enables us to borrow multi-source information to provide more effective recommender system.

Authors who are presenting talks have a * after their name.

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