Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 498, Issue -, Pages 102-115Publisher
ELSEVIER
DOI: 10.1016/j.physa.2017.12.144
Keywords
Transferring similarity; Link prediction; Dempster-Shafer evidence theory; Belief function; Recommender systems
Categories
Funding
- National Natural Science Foundation of China [61573290, 61503237]
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Recommender systems have developed along with the web science, and how to measure the similarity between users is crucial for processing collaborative filtering recommendation. Many efficient models have been proposed (i.g., the Pearson coefficient) to measure the direct correlation. However, the direct correlation measures are greatly affected by the sparsity of dataset. In other words, the direct correlation measures would present an inauthentic similarity if two users have a very few commonly selected objects. Transferring similarity overcomes this drawback by considering their common neighbors (i.e., the intermediates). Yet, the transferring similarity also has its drawback since it can only provide the interval of similarity. To break the limitations, we propose the Belief Transferring Similarity (BTS) model. The contributions of BTS model are: (1) BTS model addresses the issue of the sparsity of dataset by considering the high-order similarity. (2) BTS model transforms uncertain interval to a certain state based on fuzzy systems theory. (3) BTS model is able to combine the transferring similarity of different intermediates using information fusion method. Finally, we compare BTS models with nine different link prediction methods in nine different networks, and we also illustrate the convergence property and efficiency of the BTS model. (C) 2018 Elsevier B.V. All rights reserved.
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