Research of Mobile Recommender Service Based on User Preferences and Social Tags
Pages 6 - 12
Abstract
In current mobile recommendation service field, it is a hot spot that how to solve the complicated mobile information overload problem of the mobile environment. In the Web3.0 era, social tags are sufficient to describe users' interest preferences and implicit features, and become an effective alternative to important factors such as user preferences, contextual information, and context-aware attributes. Therefore, a tag recommendation algorithm based on user preferences and social tags (UPST-TB) with time weights is proposed. Firstly, normalized the mobile information of multidimensional data onto social tags data, making it a single data problem; Secondly, depth-weighted analysis of user-interest tag-resource triplet by using TF-IDF, weighted centrality, and heat conduction diffusion pre-processing method. Finally, recommendation by using the mobile recommendation service model under the smart network. The experimental results show that the performance of the proposed algorithm has significant improvement.
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Published In
May 2019
202 pages
ISBN:9781450371728
DOI:10.1145/3339363
Copyright © 2019 ACM.
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- Research Center for Science and Technology for Learning, National Central University, Taiwan
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 May 2019
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- Refereed limited
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CSSE 2019
CSSE 2019: 2019 2nd International Conference on Computer Science and Software Engineering
May 24 - 26, 2019
Xi'an, China
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CSSE '19 Paper Acceptance Rate 33 of 74 submissions, 45%;
Overall Acceptance Rate 33 of 74 submissions, 45%
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