Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce

• URL: http://arxiv.org/abs/2012.08952v1
• Date: Wed, 16 Dec 2020 13:52:14 GMT
• Title: Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
• Authors: Yuting Chen, Yanshi Wang, Yabo Ni, An-Xiang Zeng and Lanfen Lin
• Abstract summary: How to make accurate recommendations for users in heterogeneous e-commerce scenarios is still a continuous research topic. We propose a novel recommendation model named Scenario-aware Mutual Learning (SAML) that leverages the differences and similarities between multiple scenarios.
• Score: 12.794276204716642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users in heterogeneous e-commerce scenarios is still a continuous research topic. However, most existing studies overlook the intrinsic association of the scenarios: the log data collected from platforms can be naturally divided into different scenarios (e.g., country, city, culture). We observed that the scenarios are heterogeneous because of the huge differences among them. Therefore, a unified model is difficult to effectively capture complex correlations (e.g., differences and similarities) between multiple scenarios thus seriously reducing the accuracy of recommendation results. In this paper, we target the problem of multi-scenario recommendation in e-commerce, and propose a novel recommendation model named Scenario-aware Mutual Learning (SAML) that leverages the differences and similarities between multiple scenarios. We first introduce scenario-aware feature representation, which transforms the embedding and attention modules to map the features into both global and scenario-specific subspace in parallel. Then we introduce an auxiliary network to model the shared knowledge across all scenarios, and use a multi-branch network to model differences among specific scenarios. Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network. We conduct extensive experiments on both public and industrial datasets, empirical results show that SAML consistently and significantly outperforms state-of-the-art methods.

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