Enhancing Accuracy of Hybrid Recommender Systems through Adapting the Domain Trends.
Fatih Aksel, Ayşenur Birtürk · 2010
Hybrid recommender systems combine several algorithms based on their hybridization strategy. Prediction algorithm selection strategy directly influence the accuracy of the hy-brid recommenders. Recent research has mostly focused on static hybridization schemes which are designed as fixed combinations of prediction algorithms and do not change at run-time. However, people’s tastes and desires are tem-porary and gradually evolve. Moreover, each domain has unique characteristics, trends and unique user interests. In this paper, we propose an adaptive method for hybrid rec-ommender systems, in which the combination of algorithms are learned and dynamically updated from the results of previous predictions. We describe our hybrid recommender system, called AdaRec, that uses domain attributes to un-derstand the domain drifts and trends, and user feedback in order to change it’s prediction strategy at run-time, and adapt the combination of content-based and collaborative algorithms to have better results. Experiment results with datasets show that our system outperforms naive hybrid rec-ommender.