Abstract
Recommendation system has attracted growing attention in machine learning and data mining. In addition, one-class learning has been proposed to build a predictive classifier mainly based on the preferred data, and has been verified to be an efficient method for recommendation system. In this paper, we propose a novel recommendation model called transfer learning-based one-class dictionary learning for recommendation data stream (DOC-SVM), which incorporates transfer learning and discriminative dictionary learning into one-class data stream learning. The proposed method can reduce the effect of the uncertain data on the classifier and transfer knowledge from the recent historical chunk to the current chunk to construct a predictive classifier for the current chunk. We then propose an iterative framework to solve the optimization function, and obtain the predictive classifier for the current chunk. Extensive experiments has been conducted and the results show that the proposed method can deliver better performance than the state-of-the-art methods.
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