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Sequential Recommendation with Decomposed Item Feature Routing

Published: 25 April 2022 Publication History
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  • Abstract

    Sequential recommendation basically aims to capture user evolving preference. Intuitively, a user interacts with an item usually because of some specific feature, and user evolving preference is essentially determined by a series of important features along the time line. However, existing sequential models usually represent each item by a unified embedding, which fails to distinguish item features, let along modeling the feature sequences. To bridge this gap, in this paper, we propose a novel sequential recommender model by learning the key item feature sequences underlying user behaviors, which facilitates more focused model optimization and better recommendation performance. To achieve this goal, we firstly represent each item by explicit or latent features, and then build both soft and hard models to route optimal feature sequences. More specifically, in the soft model, we design a 2D attention mechanism, which simultaneously distinguishes the importances of the items in a sequence and the features for the same item. For the hard model, we regard the feature routing problem as a Markov decision process, and propose a reinforcement learning method to generate feature sequences, which can lead to the lowered negative log-likelihood. In the experiments, we compare our model with the state-of-the-art methods based on real-world datasets, where we can empirically demonstrate 8.2 and 16.1 improvements of our model on NDCG and MRR, respectively.

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    Cited By

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    • (2024)Unified Visual Preference Learning for User Intent UnderstandingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635858(816-825)Online publication date: 4-Mar-2024
    • (2023)SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615063(1566-1575)Online publication date: 21-Oct-2023
    • (2023)Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614775(843-853)Online publication date: 21-Oct-2023

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    cover image ACM Conferences
    WWW '22: Proceedings of the ACM Web Conference 2022
    April 2022
    3764 pages
    ISBN:9781450390965
    DOI:10.1145/3485447
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    Publication History

    Published: 25 April 2022

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    Author Tags

    1. Disentangled Representation Learning
    2. Reinforcement Learning
    3. Sequential Recommendation

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    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • Beijing Outstanding Young Scientist Program
    • Major Innovation & Planning Interdisciplinary Platform for the Double-First Class Initiative
    • National Natural Science Foundation of China
    • Public Computing Cloud, Renmin University of China
    • Intelligent Social Governance Platform
    • Renmin University of China

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    WWW '22
    Sponsor:
    WWW '22: The ACM Web Conference 2022
    April 25 - 29, 2022
    Virtual Event, Lyon, France

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

    View all
    • (2024)Unified Visual Preference Learning for User Intent UnderstandingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635858(816-825)Online publication date: 4-Mar-2024
    • (2023)SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615063(1566-1575)Online publication date: 21-Oct-2023
    • (2023)Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614775(843-853)Online publication date: 21-Oct-2023

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