Abstract
In real human society, influence on each other is an important factor in a variety of social activities. It is obviously important for recommendation. However, the influence factor is rarely taken into account in traditional recommendation algorithms. In this study, we propose an integrated approach for recommendation by analyzing and mining social data and introducing a set of new measures for user influence and social trust. Our experimental results show that our proposed approach outperforms traditional recommendation in terms of accuracy and stability.
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Li, W., Ye, Z., Jin, Q. (2014). An Integrated Recommendation Approach Based on Influence and Trust in Social Networks. In: Park, J., Pan, Y., Kim, CS., Yang, Y. (eds) Future Information Technology. Lecture Notes in Electrical Engineering, vol 309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55038-6_13
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DOI: https://doi.org/10.1007/978-3-642-55038-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55037-9
Online ISBN: 978-3-642-55038-6
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