Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce

• URL: http://arxiv.org/abs/2306.05011v1
• Date: Thu, 8 Jun 2023 07:59:08 GMT
• Title: Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
• Authors: Juan Gong, Zhenlin Chen, Chaoyi Ma, Zhuojian Xiao, Haonan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yunjiang Jiang
• Abstract summary: We propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. AW-MoE has been successfully deployed in the JD e-commerce search engine.
• Score: 21.7796124109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the user preference. Existing algorithms usually extract a user representation vector from the user behavior sequence, then feed the vector into a feed-forward network (FFN) together with other features for feature interactions, and finally produce a personalized ranking score. Despite tremendous progress in the past, there is still room for improvement. Firstly, the personalized patterns of feature interactions for different users are not explicitly modeled. Secondly, most of existing algorithms have poor personalized ranking results for long-tail users with few historical behaviors due to the data sparsity. To overcome the two challenges, we propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. Firstly, AW-MoE leverages the MoE framework to capture personalized feature interactions for different users. To model the user preference, the user behavior sequence is simultaneously fed into expert networks and the gate network. Within the gate network, one gate unit and one activation unit are designed to adaptively learn the fine-grained activation vector for experts using an attention mechanism. Secondly, a random masking strategy is applied to the user behavior sequence to simulate long-tail users, and an auxiliary contrastive loss is imposed to the output of the gate network to improve the model generalization for these users. This is validated by a higher performance gain on the long-tail user test set. Experiment results on a JD real production dataset and a public dataset demonstrate the effectiveness of AW-MoE, which significantly outperforms state-of-art methods. Notably, AW-MoE has been successfully deployed in the JD e-commerce search engine, ...

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