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Leveraging Ratings and Reviews with Gating Mechanism for Recommendation

Published: 03 November 2019 Publication History

Abstract

Recommender system plays an important role to provide people with personalized information based on their history records. However, it is still a challenge to capture the preference of users accurately due to the sparsity of rating data and the heterogeneity of review data. In this paper, we propose a hybrid deep collaborative filtering model that jointly learns latent representations from ratings and reviews. Specifically, the model learns the rating feature and textual feature based on ratings and reviews simultaneously. Two embedding layers are employed to learn rating feature for users and items based on the user and item interactions, and two attention-based GRU networks learn context-aware representation from user and item reviews. Then a gating mechanism is used to leverage contributions from rating feature and textual feature. Experimental results on six real-world datasets demonstrate the superior performance of the proposed method over several state-of-the-art methods. Moreover, the keywords in reviews can be highlighted to interpret the predictions with the attention mechanism.

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Cited By

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  • (2024)Shilling Black-Box Recommender Systems by Learning to Generate Fake User ProfilesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318321035:1(1305-1319)Online publication date: Jan-2024
  • (2024)RAKCR: Reviews sentiment-aware based knowledge graph convolutional networks for Personalized RecommendationExpert Systems with Applications10.1016/j.eswa.2024.123403248(123403)Online publication date: Aug-2024
  • (2024)CRAS: cross-domain recommendation via aspect-level sentiment extractionKnowledge and Information Systems10.1007/s10115-024-02130-666:9(5459-5477)Online publication date: 1-Sep-2024
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  1. Leveraging Ratings and Reviews with Gating Mechanism for Recommendation

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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
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 November 2019

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    Author Tags

    1. attention mechanism
    2. gated recurrent unit
    3. rating prediction
    4. recommender system

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    • the National Post-Doctoral Program for Innovative Talents
    • the National Natural Science Foundation of China
    • the Natural Science Foundation of Hubei Province
    • the National Key R & D Program of China
    • Science and Technology Major Project of Hubei Province 2019 (Next-Generation AI Technologies)

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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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    Cited By

    View all
    • (2024)Shilling Black-Box Recommender Systems by Learning to Generate Fake User ProfilesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318321035:1(1305-1319)Online publication date: Jan-2024
    • (2024)RAKCR: Reviews sentiment-aware based knowledge graph convolutional networks for Personalized RecommendationExpert Systems with Applications10.1016/j.eswa.2024.123403248(123403)Online publication date: Aug-2024
    • (2024)CRAS: cross-domain recommendation via aspect-level sentiment extractionKnowledge and Information Systems10.1007/s10115-024-02130-666:9(5459-5477)Online publication date: 1-Sep-2024
    • (2023)Multi-aspect Graph Contrastive Learning for Review-enhanced RecommendationACM Transactions on Information Systems10.1145/361810642:2(1-29)Online publication date: 8-Nov-2023
    • (2023)M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591720(1468-1477)Online publication date: 19-Jul-2023
    • (2023)Learning Aspect-Aware High-Order Representations from Ratings and Reviews for RecommendationACM Transactions on Knowledge Discovery from Data10.1145/353218817:1(1-22)Online publication date: 20-Feb-2023
    • (2023)Mobile Game Recommendation Based on Clustering Reliable Reviews2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT)10.1109/ICEICT57916.2023.10244945(72-77)Online publication date: 21-Jul-2023
    • (2022)Personalized Recommendation Using Aspect-Aware Knowledge Graph Learningundefined10.12794/metadc2048609Online publication date: Dec-2022
    • (2022)A Dual-Expert Framework for Event Argument ExtractionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531923(1110-1121)Online publication date: 6-Jul-2022
    • (2022)CFDA: Collaborative Filtering with Dual Autoencoder for Recommender System2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892705(1-7)Online publication date: 18-Jul-2022
    • Show More Cited By

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