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MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games

Published: 27 July 2024 Publication History

Abstract

Recommender system helps address information overload problem and satisfy consumers’ personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks but usually ignore how to improve the interpretability of recommendation, which is still a challenging and crucial task, especially for some complicated scenarios such as large-scale online games. A few previous attempts on explainable recommendation mostly depend on a large amount of a priori knowledge or user-provided review corpus, which is labor consuming as well as often suffers from data deficiency. To relieve this issue, we propose a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation (MHANER) for the case without enough a priori knowledge or corpus of user comments. Specifically, MHANER employs the attention mechanism to model players’ preference to in-game store items as the support for the explanation of recommendation. Then a graph neural network–based method is designed to model players’ multi-source heterogeneous information, including the players’ historical behavior data, historical purchase data, and attributes of the player-controlled character, which is leveraged to recommend possible items for players to buy. Finally, the multi-level subgraph pattern mining is adopted to combine the characteristics of a recommendation list to generate corresponding explanations of items. Extensive experiments on three real-world datasets, two collected from JD and one from NetEase game, demonstrate that the proposed model MHANER outperforms state-of-the-art baselines. Moreover, the generated explanations are verified by human encoding comprised of hard-core game players and endorsed by experts from game developers.

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Cited By

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  • (2024)Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/365131218:6(1-20)Online publication date: 12-Apr-2024

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    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 4
    August 2024
    563 pages
    EISSN:2157-6912
    DOI:10.1145/3613644
    • Editor:
    • Huan Liu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 July 2024
    Online AM: 09 October 2023
    Accepted: 19 September 2023
    Revised: 01 September 2023
    Received: 27 June 2023
    Published in TIST Volume 15, Issue 4

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    Author Tags

    1. Recommender system
    2. graph attention networks
    3. explainable recommendation
    4. graph mining

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    • Key Research and Development Program of Zhejiang Province
    • National Natural Science Foundation of China

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    • (2024)Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/365131218:6(1-20)Online publication date: 12-Apr-2024

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