Recommendation model based on intention decomposition and heterogeneous information fusion
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Published:2023
Issue:9
Volume:20
Page:16401-16420
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Zhang Suqi1, Wang Xinxin2, Wang Wenfeng2, Zhang Ningjing2, Fang Yunhao3, Li Jianxin3
Affiliation:
1. School of information engineering, Tianjin University of Commerce, Tianjin 300134, China 2. School of science, Tianjin University of Commerce, Tianjin 300134, China 3. School of IT, Deakin University, Burwood, VIC 3125, Australia
Abstract
<abstract>
<p>In order to solve the problem of timeliness of user and item interaction intention and the noise caused by heterogeneous information fusion, a recommendation model based on intention decomposition and heterogeneous information fusion (IDHIF) is proposed. First, the intention of the recently interacting items and the users of the recently interacting candidate items is decomposed, and the short feature representation of users and items is mined through long-short term memory and attention mechanism. Then, based on the method of heterogeneous information fusion, the interactive features of users and items are mined on the user-item interaction graph, the social features of users are mined on the social graph, and the content features of the item are mined on the knowledge graph. Different feature vectors are projected into the same feature space through heterogeneous information fusion, and the long feature representation of users and items is obtained through splicing and multi-layer perceptron. The final representation of users and items is obtained by combining short feature representation and long feature representation. Compared with the baseline model, the AUC on the Last.FM and Movielens-1M datasets increased by 1.83 and 4.03 percentage points, respectively, the F1 increased by 1.28 and 1.58 percentage points, and the Recall@20 increased by 3.96 and 2.90 percentage points. The model proposed in this paper can better model the features of users and items, thus enriching the vector representation of users and items, and improving the recommendation efficiency.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
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