HIGnet: Hierarchical and Interactive Gate Networks for Item Recommendation
2020; Institute of Electrical and Electronics Engineers; Volume: 35; Issue: 5 Linguagem: Inglês
10.1109/mis.2020.3005928
ISSN1941-1294
AutoresMingyang Zhong, Chaojie Li, Jiahui Wen, Liangchen Liu, Jingwei Ma, Guangda Zhang, Yin Yang,
Tópico(s)Advanced Graph Neural Networks
ResumoExisting research exploits the semantic information from reviews to complement user-item interactions for item recommendation. However, as these approaches either defer the user-item interactions until the prediction layer or simply concatenate all the reviews of a user/item into a single review, they fail to capture the complex correlations between each user-item pair or introduce noises. Thus, we propose a novel Hierarchical and Interactive Gate Network (HIGnet) model for rating prediction. Modeling local word informativeness and global review semantics in a hierarchical manner enable us to exploit textual features of users/items and capture complex semantic user-item correlations at different levels of granularities. Experiments on five challenging real-world datasets demonstrate the state-of-the-art performance of the proposed HIGnet model. To facilitate community research, the implementation of the proposed model is made publicly available (https://github.com/uqjwen/higan).
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