Matrix Factorization with Content Relationships for Media Personalization
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Summary
This paper proposes a novel method, called Content Relationships Matrix Factorization (CRMF), which exploits additional information in the form of content relationships that express relevance between items, which compares favorably to the baseline method, demonstrating the usefulness of considering content relationships.
- Type
- article
- Published
- 2013-01-01
- Cited by
- 2
- References
- 17
- Access
- Open access
- OpenAlex
- https://openalex.org/W53041923
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11201799
Keywords
Personalization, Matrix decomposition, Recommender system, Computer science, Exploit
References
- Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering
- The Million Song Dataset
- Recommender Systems for Social Tagging Systems
- Matrix Factorization Techniques for Recommender Systems
- A Survey of Collaborative Filtering Techniques
- Content-boosted collaborative filtering for improved recommendations
- A matrix factorization technique with trust propagation for recommendation in social networks
- Probabilistic Matrix Factorization
- Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
- Collaborative filtering recommender systems
- Information overload
- Recommender Systems Handbook
- Generalized Cores
- Generalized Cores
- Content-Based Recommendation Systems
- Hybrid Web Recommender Systems
- Introduction to Recommender Systems Handbook
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