{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/joint-topic-semantic-aware-social","title":"Joint Topic-Semantic-aware Social Recommendation for Online Voting","arxiv_id":"1712.00731","date":"2017-12-03","proceeding":null,"authors":["Hongwei Wang","Jia Wang","Miao Zhao","Jiannong Cao","Minyi Guo"],"abstract":"Online voting is an emerging feature in social networks, in which users can\nexpress their attitudes toward various issues and show their unique interest.\nOnline voting imposes new challenges on recommendation, because the propagation\nof votings heavily depends on the structure of social networks as well as the\ncontent of votings. In this paper, we investigate how to utilize these two\nfactors in a comprehensive manner when doing voting recommendation. First, due\nto the fact that existing text mining methods such as topic model and semantic\nmodel cannot well process the content of votings that is typically short and\nambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to\nlearn word and document representation by jointly considering their topics and\nsemantics. Then we propose our Joint Topic-Semantic-aware social Matrix\nFactorization (JTS-MF) model for voting recommendation. JTS-MF model calculates\nsimilarity among users and votings by combining their TEWE representation and\nstructural information of social networks, and preserves this\ntopic-semantic-social similarity during matrix factorization. To evaluate the\nperformance of TEWE representation and JTS-MF model, we conduct extensive\nexperiments on real online voting dataset. The results prove the efficacy of\nour approach against several state-of-the-art baselines.","url_abs":"http://arxiv.org/abs/1712.00731v1","url_pdf":"http://arxiv.org/pdf/1712.00731v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"joint-topic-semantic-aware-social","repo_url":"https://github.com/hwwang55/JTS-MF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}