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Who Will Follow Your Shop? Exploiting Multiple Information Sources in Finding Followers

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7826)

Abstract

WuXianGouXiang is an O2O(offline to online and vice versa)-based mobile application that recommends the nearby coupons and deals for users, by which users can also follow the shops they are interested in. If the potential followers of a shop can be discovered, the merchant’s targeted advertising can be more effective and the recommendations for users will also be improved. In this paper, we propose to predict the link relations between users and shops based on the following behavior. In order to better model the characteristics of the shops, we first adopt Topic Modeling to analyze the semantics of their descriptions and then propose a novel approach, named INtent Induced Topic Search (INITS) to update the hidden topics of the shops with and without a description. In addition, we leverage the user logs and search engine results to get the similarity between users and shops. Then we adopt the latent factor model to calculate the similarity between users and shops, in which we use the multiple information sources to regularize the factorization. The experimental results demonstrate that the proposed approach is effective for detecting followers of the shops and the INITS model is useful for shop topic inference.

Keywords

  • User Behavior
  • Location Based Services
  • Matrix Factorization

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  • DOI: 10.1007/978-3-642-37450-0_30
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Wu, L. et al. (2013). Who Will Follow Your Shop? Exploiting Multiple Information Sources in Finding Followers. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_30

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  • DOI: https://doi.org/10.1007/978-3-642-37450-0_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37449-4

  • Online ISBN: 978-3-642-37450-0

  • eBook Packages: Computer ScienceComputer Science (R0)