Factor Models for Tag Recommendation in BibSonomy
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Summary
This approach to the ECML/PKDD Discovery Challenge 2009 tries to find latent interactions between users, items and tags by factorizing the observed tagging data by learning the Bayesian Personal Ranking method.
- Type
- article
- Published
- 2009-09-07
- Cited by
- 48
- References
- 3
- OpenAlex
- https://openalex.org/W77971441
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17793464
Keywords
Computer science, Overfitting, Ranking (information retrieval), Bayesian probability, Hyperparameter
References
Cited by
- Social Network and Click-through Prediction with Factorization Machines
- Context-Aware Ranking with Factorization Models
- Tag recommendation using folksonomy information for online sound sharing platforms
- Latency of Neighborhood Based Recommender Systems
- fastFM: A Library for Factorization Machines
- Recommender Systems for Social Tagging Systems
- Resource recommendation in social annotation systems: A linear-weighted hybrid approach
- Improving the Accuracy and Efficiency of Tag Recommendation System by Applying Hybrid Methods
- Factorization models for context-/time-aware movie recommendations
- Folksonomy-Based Tag Recommendation for Collaborative Tagging Systems
- Pairwise interaction tensor factorization for personalized tag recommendation
- Hybrid tag recommendation for social annotation systems
- A parameter-free algorithm for an optimized tag recommendation list size
- Class-based tag recommendation and user-based evaluation in online audio clip sharing
- A Probabilistic Approach to Personalized Tag Recommendation
- Convex Co-embedding
- Personalized Ranking for Non-Uniformly Sampled Items
- HYBRID TAG RECOMMENDATION IN COLLABORATIVE TAGGING SYSTEMS
- The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems
- Modeling Activation Processes in Human Memory to Predict the Use of Tags in Social Bookmarking Systems
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