Deep Attentive Factorization Machine for App Recommendation Service

Recommendation service in mobile app markets decently helps users choose their preferred apps. Though a lot of recommendation service models are proposed in recent years, it is still challenging to tackle extreme sparse app data and get a relatively satisfactory recommendation performance. The reaso...

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Bibliographic Details
Published in2019 IEEE International Conference on Web Services (ICWS) pp. 134 - 138
Main Authors Guo, Chenkai, Xu, Yifan, Hou, Xiaolei, Dong, Naipeng, Xu, Jing, Ye, Quanqi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
app recommendation
attention mechanism
Computational modeling
Data models
deep learning
factorization machine
Feature extraction
Frequency modulation
Knowledge based systems
mobile app
Mobile applications
Predictive models
rating prediction
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Summary:Recommendation service in mobile app markets decently helps users choose their preferred apps. Though a lot of recommendation service models are proposed in recent years, it is still challenging to tackle extreme sparse app data and get a relatively satisfactory recommendation performance. The reason can be concluded as that traditional recommendation models either focus on limited features or stand aside from deep training. In this paper, we propose knowledge-based deep factorization machine (KDFM), a recommendation model inspired by techniques of factorization machine and attentive deep learning, and apply it in the recommendation service for mobile apps. The KDFM aims to make full use of the rich categorical and textual knowledge in the app market for better performance. To achieve this goal, a topical attention representation component, which contains three typical parts (Word2Vec, BiLSTM and Topical Attention), is constructed. Such representation not only avoids the dimension explosion brought by traditional models, but also preserves the textual semantics for better recommendation. Through extensive experiments conducted on a large number of collected app samples, the KDFM achieves better performance compared with state-of-art rating recommendation models in terms of the rating prediction. In addition, the benefits brought by the usage of attention mechanism and topical representation are confirmed through the comparison experiments.
DOI:10.1109/ICWS.2019.00032