Abstract
Three main approaches in Context-Aware Recommender Systems (CARSs) are pre-filtering, post-filtering and contextual modeling. Incorporating contextual information into main process is the different point of contextual modeling from two first approaches. In this paper, we first propose a new context-aware collaborative filtering (CACF) algorithm with contextual modeling approach combined from a clustering technique and matrix factorization method named Similar Trends Identifying (STI). We then compare the proposal with various matrix factorization-based algorithms. Overall, the STI algorithm outperforms some compared algorithms in terms of evaluation metrics and available contextual data sets.
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Do, P., Le, H., Nguyen, V.T., Dung, T.N. (2014). A Context-Aware Collaborative Filtering Algorithm through Identifying Similar Preference Trends in Different Contextual Information. In: Jeong, H., S. Obaidat, M., Yen, N., Park, J. (eds) Advances in Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41674-3_49
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DOI: https://doi.org/10.1007/978-3-642-41674-3_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41673-6
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