Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
IEEE Transactions on Knowledge and Data Engineering
Use of social network information to enhance collaborative filtering performance
Expert Systems with Applications: An International Journal
Inferring relevant social networks from interpersonal communication
Proceedings of the 19th international conference on World wide web
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Predicting performance in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
A comparative study of heterogeneous item recommendations in social systems
Information Sciences: an International Journal
A Random Walk Model for Item Recommendation in Social Tagging Systems
ACM Transactions on Management Information Systems (TMIS)
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Ensemble recommender systems successfully enhance recom-mendation accuracy by exploiting different sources of user prefe-rences, such as ratings and social contacts. In linear ensembles, the optimal weight of each recommender strategy is commonly tuned empirically, with limited guarantee that such weights are optimal afterwards. We propose a self-adjusting hybrid recommendation approach that alleviates the social cold start situation by weighting the recommender combination dynamically at recommendation time, based on social network analysis algorithms. We show empirical results where our approach outperforms the best static combination for different hybrid recommenders.