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.
Original language | English |
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Title of host publication | CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management |
Publisher | Association for Computing Machinery |
Pages | 347-356 |
Number of pages | 10 |
Volume | Part F131841 |
ISBN (Electronic) | 9781450349185 |
DOIs | |
Publication status | Published - 6 Nov 2017 |
Event | 26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Pan Pacific Singapore Hotel, Singapore, Singapore Duration: 6 Nov 2017 → 10 Nov 2017 |
Conference
Conference | 26th ACM International Conference on Information and Knowledge Management, CIKM 2017 |
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Country/Territory | Singapore |
City | Singapore |
Period | 6/11/17 → 10/11/17 |
Keywords
- Matrix factorization
- Online voting
- Recommender systems
- Topic-enhanced word embedding
ASJC Scopus subject areas
- General Business,Management and Accounting
- General Decision Sciences