Abstract
Collaborative filtering algorithms become losing its effectiveness on case that the dataset is sparse. When user ratings are scared, it’s difficult to find real similar users, which causes performance reduction of the algorithm. We here present a 3-dimension collaborative filtering framework which can use features of users and items for similarity computation to deal with the data sparsity problem. It uses feature and rating combinations instead of only ratings in collaborative filtering process and performs a more complete similarity computation. Specifically, we provide a weighted feature form and a Bayesian form in its implementation. The results demonstrate that our methods can obviously improve the performance of collaborative filtering when datasets are sparse.
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The author gratefully acknowledges the generous support from the Doctoral Fund of Shandong Jianzhu University (XNBS1527).
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Guan, Z. (2018). Multi-feature Collaborative Filtering Recommendation for Sparse Dataset. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_27
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DOI: https://doi.org/10.1007/978-3-319-93818-9_27
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