HIGnet: Hierarchical and Interactive Gate Networks for Item Recommendation

Existing 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, th...

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Bibliographic Details
Published in:IEEE intelligent systems 2020-09, Vol.35 (5), p.50-61
Main Authors: Zhong, Mingyang, Li, Chaojie, Wen, Jiahui, Liu, Liangchen, Ma, Jingwei, Zhang, Guangda, Yang, Yin
Format: Article
Language:English
Subjects:
Correlation
Feature extraction
Intelligent systems
Logic gates
Neural networks
Predictive models
Semantics
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Summary:Existing 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).
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2020.3005928