Poster Abstract: Personalized Recommendation Based on the Shopper’s Behavior Patterns from Shopping Habits
نویسندگان
چکیده
In this paper, we propose a scheme to push personalized recommendation and advertisement services for the shopping people based on behavior patterns, inferring by significant shopping stores in the shopping mall from mobile phone data. The store-level localization in the shopping mall is obtained by sparse calibration orientated semi-supervised machine learning method, called Semi-supervised Extreme Learning Machine (SELM). Then, significant shopping stores are discovered by matching the structure layout of shopping mall. In the mining phase, the scheme aims to mine diverse types of meaningful behavior patterns from the shopper’s shopping habits, which can be explored to push personalized recommendation and advertisement services accurately and reasonably.
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