Abstract
Predicting user likes in online media and recommending related products to the user would bring great profits to certain service providers. Therefore, prediction approaches have become a popular research topic in both industry and academia over the past decades. However, data sparsity makes many well-known prediction algorithms perform poorly in cold start situation. In this paper, we apply attributes from user profile in social network sites to help recommending user likes in a video sharing site and propose a model to conceptualize unstructured words in the profile attributes into interests vector by knowledge base. Based on the model, we designed a recommendation framework to predict user clicks. Experiment results on dataset show that our approach is an efficient one.
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Liu, Q., Wang, Y., Li, J., Jia, Y., Ren, Y. (2014). Predicting User Likes in Online Media Based on Conceptualized Social Network Profiles. In: Han, W., Huang, Z., Hu, C., Zhang, H., Guo, L. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8710. Springer, Cham. https://doi.org/10.1007/978-3-319-11119-3_8
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DOI: https://doi.org/10.1007/978-3-319-11119-3_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11118-6
Online ISBN: 978-3-319-11119-3
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