Abstract
The current microblog recommendation approaches mainly consider users’ interests. But because user’s interests are changing dynamically and they have low activity, it’s hard to build user interest model. In this paper, we propose a new approach to recommend information based on multiaspect similarities of interest and new dynamic strategy for defining long-term and short-term interests according to user’s interest changing. Recommended information is ranked by two factors: the similarity between user’s interest and information, tie-strength of user interest. We implemented three recommendation engines based on Sina Micro-blog and deployed them online to gather feedback from real users. Experimental results show that this method can recommend interesting information to users and improve the precision and stability of personalized information recommendation by 30 %.
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Zhang, J., Lei, Y. (2015). Improving Content Recommendation in Social Streams via Interest Model. In: Lee, R. (eds) Computer and Information Science. Studies in Computational Intelligence, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-10509-3_5
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DOI: https://doi.org/10.1007/978-3-319-10509-3_5
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