Abstract
Recommendation systems provide us a promising approach to deal with the information overload problem. Collaborative filtering is the key technology in these systems. In the past decades, model-based and memory-based methods have been the main research areas of collaborative filtering. Empirically, model-based methods may achieve higher prediction accuracy than memory-based methods. On the other side, memory-based methods (e.g. slope one algorithm) provide a concise and intuitive justification for the computed predictions. In order to take advantages of both model-based and memory-based methods, we propose a new approach by introducing the idea of machine learning to slope one algorithm. Several strategies are presented in this paper to catch this goal. Experiments on the MovieLens dataset show that our approach achieves great improvement of prediction accuracy.
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Wang, Y., Yin, L., Cheng, B., Yu, Y. (2012). Learning to Recommend Based on Slope One Strategy. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_47
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DOI: https://doi.org/10.1007/978-3-642-29253-8_47
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