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SDM: Sequential Deep Matching Model for Online Large-scale Recommender System

Published: 03 November 2019 Publication History

Abstract

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.

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
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Published: 03 November 2019

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  1. deep matching
  2. sequential recommendation

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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  • (2025)Personalized Dual Transformer Network for sequential recommendationNeurocomputing10.1016/j.neucom.2024.129244622(129244)Online publication date: Mar-2025
  • (2024)A Time-Sensitive Graph Neural Network for Session-Based New Item RecommendationElectronics10.3390/electronics1301022313:1(223)Online publication date: 3-Jan-2024
  • (2024)A feature-aware long-short interest evolution network for sequential recommendationIntelligent Data Analysis10.3233/IDA-23028828:3(733-750)Online publication date: 28-May-2024
  • (2024)Aggregating knowledge and collaborative information for sequential recommendationIntelligent Data Analysis10.3233/IDA-22719828:1(279-298)Online publication date: 3-Feb-2024
  • (2024)Generative Retrieval with Semantic Tree-Structured Identifiers and Contrastive LearningProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698408(154-163)Online publication date: 8-Dec-2024
  • (2024)A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity DynamicsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688145(433-443)Online publication date: 8-Oct-2024
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