Collaborative filtering recommender systems based on popular tags
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Summary
A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using thepopular tags, finally generates users' topic interests based on the popular tags.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 11
- References
- 17
- Access
- Open access
- OpenAlex
- https://openalex.org/W4142389
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17870665
Keywords
Computer science, Recommender system, Ambiguity, Collaborative filtering, Information retrieval
References
- Hybrid Recommender Systems: Survey and Experiments
- Application of Dimensionality Reduction in Recommender System - A Case Study
- Can all tags be used for search?
- Integrating tags in a semantic content-based recommender
- Tag-aware recommender systems by fusion of collaborative filtering algorithms
- tagging, communities, vocabulary, evolution
- Social tag prediction
- Exploring the Value of Folksonomies for Creating Semantic Metadata
- Tag-based social interest discovery
- Social tags: meaning and suggestions
- Contextualising tags in collaborative tagging systems
- Social information filtering: algorithms for automating “word of mouth”
- Collaborative Filtering Recommender Systems Using Tag Information
- Optimizing web search using social annotations
- Tagommenders: connecting users to items through tags
- Contextualising Tags in Collaborative Tagging Systems
Cited by
- Leveraging tagging data for recommender systems
- SimTrust: A New Method of Trust Network Generation
- Identifying Influential Taggers in Trust-Aware Recommender Systems
- Improving recommendation accuracy based on item-specific tag preferences
- Capturing implicit user influence in online social sharing
- Trust for Intelligent Recommendation
- A Measuring Method for User Similarity based on Interest Topic
- Developing Trust Networks Based on User Tagging Information for Recommendation Making
- Rating items by rating tags
- Measuring Attention Intensity to Web Pages Based on Specificity of Social Tags
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