Abstract
Currently, context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware collaborative filtering (CF) recommendation, but the contextual parameters in current approaches have same weights for all users. In this paper we propose an approach to learn the weights of contextual parameters for every user based on back-propagation (BP) neural network (NN). Then we present how to predict ratings based on well-known Slope One CF to achieve personalized context-aware (PC-aware) recommendation. Finally, we experimentally evaluate our approach and compare it to Slope One and context-aware CF. The experiment shows that our approach provide better recommendation results than them.
This work is supported by National Social Sciences Foundation of China under Grant No. ACA07004-08, Postdoctoral Science Foundation of China under Grant No. 20080440699, Natural Science Foundation Project of CQ CSTC under Grant No. 2008BB2183, and the Chongqing University "Project 211 - Phase 3" Visiting Exchange Program.
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Gao, M., Wu, Z. (2009). Personalized Context-Aware Collaborative Filtering Based on Neural Network and Slope One. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2009. Lecture Notes in Computer Science, vol 5738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04265-2_15
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DOI: https://doi.org/10.1007/978-3-642-04265-2_15
Publisher Name: Springer, Berlin, Heidelberg
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