Journal
KNOWLEDGE-BASED SYSTEMS
Volume 187, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2019.06.029
Keywords
Mobile application recommendation; Factorization machines; Attention network; Multi-view feature
Categories
Funding
- NSFC, China [61672453, 61672313, 61702568, U1711267]
- Subject of the Major Commissioned Project Research on China's Image in the Big Data of Zhejiang Province's Social Science Planning Advantage Discipline Evaluation and Research on the Present Situation of China's Image [16YSXK01ZD-2YB]
- Ministry of Education of China [2017PT18]
- Zhejiang University Education Foundation, China [K18-511120-004, K17-511120-017, K17-518051-021]
- National Key Research and Development Program, China [2017YFB0202200]
- NSF of China [IIS-1526499, CNS-1626432]
- Major Scientific Project of Zhejiang Lab, China [2018DG0ZX01]
- Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X355]
- Fundamental Research Funds for the Central Universities, China [17lgpy117]
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Mobile app recommendation has been an effective solution to overcoming the information overload in mobile app markets. Recent studies have demonstrated the power of neural network in recommendation tasks which is however rarely exploited for mobile apps. As one of the development of neural network, attention-based models have shown promising results for recommendation because of its capability of filtering out uninformative features from raw inputs. In this paper, to effectively predict users' preferences for apps, we propose a hierarchical neural network model called MV-AFM for app recommendation which models the interactions of features from different views (view interactions for short) through the attention mechanism. Specifically, the novelty of MV-AFM is the introduction of view segmentation for feature interactions and the construction of two level attention networks: the feature-level attention, starting from the feature embeddings within each view, which intends to select the representative features for the view, and the view-level attention, which learns the importance of interactions between any two views. Extensive experiments on two real-world mobile app datasets demonstrate the effectiveness of MV-AFM. (C) 2019 Published by Elsevier B.V.
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