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MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games.
- Source :
-
ACM Transactions on Intelligent Systems & Technology . Aug2024, Vol. 15 Issue 4, p1-23. 23p. - Publication Year :
- 2024
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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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21576904
- Volume :
- 15
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- ACM Transactions on Intelligent Systems & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 179297569
- Full Text :
- https://doi.org/10.1145/3626243