Self-adjusting hybrid recommenders based on social network analysis.

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.