Abstract
Collaborative filtering (CF)-based methods in recommender systems believe that the user’s preference of an item is the aggregation of the similar items or users. However, conventional item-based or user-based CF methods only consider either the item similarity or the user similarity. In this paper, we present hybrid-based methods for generating top-N recommendations in which both the item-item and user-user similarities are captured by the dot product of two low dimensional latent factor matrices. These matrices are learned using a stochastic gradient descent (SGD) algorithm to minimize two different loss functions, one is the squared error loss function and the other is the logistic loss function. A comprehensive set of experiments on multiple datasets is conducted to evaluate the performance of the proposed methods. The experimental results demonstrate the factored hybrid similarity methods (FHSM) achieve a superior recommendation quality in comparison with state-of-the-art methods.
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Xin, X., Wang, D., Ding, Y., Lini, C. (2016). FHSM: Factored Hybrid Similarity Methods for Top-N Recommender Systems. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_8
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DOI: https://doi.org/10.1007/978-3-319-45817-5_8
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