Meta-recommendation systems: user-controlled integration of diverse recommendations
Proceedings of the eleventh international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A hybrid approach for improving predictive accuracy of collaborative filtering algorithms
User Modeling and User-Adapted Interaction
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Combating spam in tagging systems: An evaluation
ACM Transactions on the Web (TWEB)
Integrating Folksonomies with the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Using Tag Co-occurrence for Recommendation
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
The adaptive web: methods and strategies of web personalization
The adaptive web: methods and strategies of web personalization
International Journal of Approximate Reasoning
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.