ACNN-FM: A novel recommender with attention-based convolutional neural network and factorization machines

With the rapid development of the Internet, the data generated from application platforms such as online shopping, e-education, and digital entertainment has exhibited dramatical growth, which has caused serious information overload to Internet users. The traditional recommendation approaches are cr...

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Bibliographic Details
Published in:Knowledge-based systems 2019-10, Vol.181, p.104786, Article 104786
Main Authors: Pang, Guangyao, Wang, Xiaoming, Hao, Fei, Xie, Jiehang, Wang, Xinyan, Lin, Yaguang, Qin, Xueyang
Format: Article
Language:English
Subjects:
Artificial neural networks
Attention mechanism
CAI
Cold starts
Computer assisted instruction
Convolutional neural network
Distance learning
Factorization
Factorization machines
Feature extraction
Internet
Machine learning
Neural networks
Recommendation
Recommender systems
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Summary:With the rapid development of the Internet, the data generated from application platforms such as online shopping, e-education, and digital entertainment has exhibited dramatical growth, which has caused serious information overload to Internet users. The traditional recommendation approaches are crucial for Internet users to extract valuable information from various information. However, there exist some problems such as sparse data, cold start, and over-reliance on manual extracted feature and so on. To address the above problems, this paper proposes a novel recommender with Attention-based Convolutional Neural Network and Factorization Machines (ACNN-FM), which achieves the recommendation with comments. Firstly, from the perspective of local to overall, this paper proposes a word-level attention mechanism and a phrase-level attention mechanism to increase the ability to remember the importance and the order of historical vocabulary (phrase) in the process of text processing of convolutional neural networks. Secondly, it constructs a model to automatically extract hidden features of users and items from comments in the form of natural language. Finally, we utilize factorization machines to analyze the association between the hidden features of users and items, and implement the recommendation based on the association. Extensive experiments are conducted for demonstrating that ACNN-FM method outperforms state-of-the-art NARR method, and ACNN-FM has the highest data utilization among NARR, DeepCoNN, BCF and NMF methods, thus the recommendation performance is significantly improved in large-scale data environment.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.05.029