A Deep Learning Recommendation Model with Item Audience Feature

被引：0
作者：
Wang, Yong [1 ]
Chen, Junyu [1 ]
Liu, Dong [1 ]
Deng, Jiangzhou [1 ]
机构：
[1] School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing
来源：
Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Attention Mechanism; Feature Crossing; Item Audience; Neural Networks; Recommendation Model;
D O I：
10.11925/infotech.2096-3467.2022.1098
中图分类号：
学科分类号：
摘要：
[Objective] This paper proposes a deep learning recommendation model with item audience features. It captures collaborative information and the high-order features from users and items the interactions. [Methods] First,we used the attention mechanism to analyze the historical interaction information between items and users. Then, the system adaptively constructed personalized audience features of items. Third, we introduced these features to the model as important supplementary information for preference predictions. We also developed an explicit feature crossing and introduced residual connections to enrich the high-order features. [Results] We examined the new model with three public datasets. It improved the Precision, Recall, F1, and NDCG by up to 9.1%, 9.4%, 9.2%, and 12.1% compared with the sub-optimal method (the recommendation length = 10). [Limitations] The performance of our model relies mainly on the historical interaction data volumes. [Conclusions] The proposed model improves the recommendation quality and shows good application potential. © 2023 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：114 / 124
页数：10
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