SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
| Autor: | Fei Sun, Fuyu Lv, Quan Lin, Changlong Yu, Keping Yang, Taiwei Jin, Wilfred Ng |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Matching (statistics) Computer science business.industry 02 engineering and technology Recommender system Machine learning computer.software_genre Session (web analytics) Computer Science - Information Retrieval Term (time) Range (mathematics) 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering Embedding 020201 artificial intelligence & image processing Artificial intelligence business computer Information Retrieval (cs.IR) |
| Zdroj: | CIKM |
| DOI: | 10.48550/arxiv.1909.00385 |
| Popis: | Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry. However, they are not effective to model dynamic and evolving preferences of users. In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors. Compared with existing sequence-aware recommendation methods, we tackle the following two inherent problems in real-world applications: (1) there could exist multiple interest tendencies in one session. (2) long-term preferences may not be effectively fused with current session interests. Long-term behaviors are various and complex, hence those highly related to the short-term session should be kept for fusion. We propose to encode behavior sequences with two corresponding components: multi-head self-attention module to capture multiple types of interests and long-short term gated fusion module to incorporate long-term preferences. Successive items are recommended after matching between sequential user behavior vector and item embedding vectors. Offline experiments on real-world datasets show the superior performance of the proposed SDM. Moreover, SDM has been successfully deployed on online large-scale recommender system at Taobao and achieves improvements in terms of a range of commercial metrics. Comment: 9 pages, CIKM 2019 camera ready version |
| Databáze: | OpenAIRE |
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