Conference proceeding
Leveraging User Interest to Improve Thread Recommendation in Online Forum
2013 INTERNATIONAL CONFERENCE ON SOCIAL INTELLIGENCE AND TECHNOLOGY (SOCIETY)
01 Jan 2013
Abstract
Nowadays thread recommendation is considered to be beneficial to improve the end-user stickiness of an online forum. Given the fact of information overload and the diverse interests of forum users, a recommender system in online forum can satisfy not only forum users' information needs by directing them to what they might be interested in, but also their social needs by connecting them to their friends. Some traditional recommender systems rely on a bipartite graph model to capture users' interests. As an extension, some other content-based methods are proposed to further understand the potential connections between Web users and Web contents. However, due to the prevalence of short and sparse messages in online social media, it is hard for traditional content-based methods to capture Web users' interests. In this paper, we propose a novel graphical model to extract hidden topics from Web contents, cluster Web contents into clusters, and detect users' interests on each cluster. Then we introduce two reranking models which utilize the detected user interest to boost the performance of thread recommendation. Experiment results on a public dataset showed that our proposed methods substantially outperformed the naive content-based approach. In addition, by testing our approaches with different parameter settings, we observed, to some extent, how forum users' information needs and their social needs interplay to decide which threads they will look for.
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Details
- Title
- Leveraging User Interest to Improve Thread Recommendation in Online Forum
- Creators
- Xuning Tang - Drexel UniversityMi Zhang - Drexel UniversityChristopher C. Yang - Drexel UniversityIEEE
- Publication Details
- 2013 INTERNATIONAL CONFERENCE ON SOCIAL INTELLIGENCE AND TECHNOLOGY (SOCIETY)
- Conference
- 2013 INTERNATIONAL CONFERENCE ON SOCIAL INTELLIGENCE AND TECHNOLOGY (SOCIETY)
- Publisher
- IEEE
- Number of pages
- 9
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000325010200002
- Scopus ID
- 2-s2.0-84881164173
- Other Identifier
- 991019168915204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic