Abstract
Due to the rapid development of E-commerce, personalized recommendations have been indispensable. The conventional user-based collaborative filtering (CF) cannot well satisfy users’ requirements, besides the recommendation results are not accurate enough. To improve the conventional user-based CF, this paper proposes an adaptive CF method based on multiple features. We take four considerations into account: 1) redefining itemitem/ user-user similarity by utilizing item/user vector; 2) making predictions based on the relation between the predicted item and the rated similar items; 3) modifying the rating according to the interest in the type of item; 4) improving the diversity of recommendation. The proposed method is easy to implement, and experimental results based on two well-known datasets have demonstrated the superiority in accuracy and diversity.
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Zhang, YQ., Zheng, HT., Zhang, LS. (2013). An Adaptive Collaborative Filtering Algorithm Based on Multiple Features. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_41
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DOI: https://doi.org/10.1007/978-3-642-53917-6_41
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