Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75042
Title: Joint topic-semantic-aware social recommendation for online voting
Authors: Wang, H 
Wang, J 
Zhao, M 
Cao, J 
Guo, M
Keywords: Matrix factorization
Online voting
Recommender systems
Topic-enhanced word embedding
Issue Date: 2017
Publisher: Association for Computing Machinery
Source: International Conference on Information and Knowledge Management, Proceedings, 2017, v. Part F131841, p. 347-356 How to cite?
Abstract: Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.
Description: 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Pan Pacific, Singapore, 6-10 November 2017
URI: http://hdl.handle.net/10397/75042
ISBN: 9781450349185
DOI: 10.1145/3132847.3132889
Appears in Collections:Conference Paper

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