Conference proceeding
User Interest and Topic Detection for Personalized Recommendation
2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, v 1, pp 442-446
01 Jan 2012
Abstract
Recommender system provides users with personalized suggestions of product or information. Typically, recommender systems rely on a bipartite graph model to capture user interest. As an extension, some boosted methods analyze content information to further improve the quality of personalized recommendation. However, due to the prevalence of short and sparse messages in online social media, traditional content-boosted methods do not guarantee to capture user preference accurately especially for web contents. In this paper, we propose a novel graphical model to extract hidden topics from web contents, cluster web contents, and detect users' interests on each cluster. In addition, we introduce two reranking models which utilize the detected user interest to further boost the quality of personalized recommendation. Experiment results on a public dataset demonstrated the limitation of a traditional content-boosted approach, and also showed the validity of our proposed techniques.
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Details
- Title
- User Interest and Topic Detection for Personalized Recommendation
- Creators
- Xuning Tang - Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USAMi Zhang - Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USAChristopher C. Yang - Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA
- Contributors
- N Zhong (Editor)Z Gong (Editor)Y M Cheung (Editor)P Lingras (Editor)P S Szczepaniak (Editor)E Suzuki (Editor)
- Publication Details
- 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, v 1, pp 442-446
- Conference
- 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012)
- Publisher
- IEEE
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000423016400064
- Scopus ID
- 2-s2.0-84878459794
- Other Identifier
- 991019168028204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic