Abstract
In this paper, we propose a new approach to predict users’ interest eigenvalues based on multi-Markov chain model, which provides a better personalized service for the users timely. We first collect a dataset from Sina Weibo that includes 4613 users and more than 16 million messages; Then, preprocess data set to obtain users’ interest eigenvalues. After that, divide users into several categories and establish multi-Markov chain to predict users’ interest eigenvalues. Our experiments show that using multi-Markov model to predict users’ interest eigenvalues is feasible and efficient, and could predicting both long-term and short-term user interests based on a suitable selection of the initial state distribution, λ.
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Acknowledgements
The authors would like to thank the support of the Technology Innovation Platform Project of Fujian Province under Grant No. 2009J1007, the Program of Fujian Key Project under Grant No. 2013H6011, the Natural Science Foundation of Fujian Province under Grant No. 2013J01228.
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An, D., Zheng, X. (2015). Markov Based Social User Interest Prediction. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_35
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DOI: https://doi.org/10.1007/978-3-319-26181-2_35
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