Abstract
Social trust prediction aims at predicting the missing trust relations between online users. In this paper, we propose a novel and scalable structured sparse linear model for social trust prediction from a global neighborhood-based collaborative filtering perspective. We formulate the prediction problem as a set of independent linear regression problems regularized by pairwise elastic net, to automatically learn correlation coefficients between a user and its most similar neighbors. In order to deal with large-scale sparse social trust data, we utilize efficient hashing techniques and stochastic coordinate descent algorithm to cut down the computational cost of training model. The experimental results on three real-world data sets show that our approach can significantly outperform the other tested methods in terms of prediction quality and efficiency.
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Yi, D., Zhang, Y., Wang, Y., Wei, B. (2014). Structured Sparse Linear Model for Social Trust Prediction. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_82
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DOI: https://doi.org/10.1007/978-3-319-08010-9_82
Publisher Name: Springer, Cham
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