Abstract
According to the records of the user’s past shopping and the properties of merchandise, recommended related products is a core function of online shopping, the feature also has an important value in the search of the Internet page, is emerging data mining research field. Its core is that recommending maybe interested information to the user. This subject according to user feedback records of the shopping system or retrieval system , using the cluster analysis techniques to identify the relationship among objects, and analyzing cluster characteristics of the object semantics to support the efficient product recommendations and sort of related information .In order to effectively improve commodity recommendation and the sorting effect of related information, this paper suggested that with the user access records and feedback record of shopping system or retrieval system analysis of relationship between objects independently, to support the efficient product recommendations and sort of related information.
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Yin, Z., Yu, X., Zhang, H. (2014). Commodity Recommendation Algorithm Based on Social Network. In: Jeong, H., S. Obaidat, M., Yen, N., Park, J. (eds) Advances in Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41674-3_5
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DOI: https://doi.org/10.1007/978-3-642-41674-3_5
Publisher Name: Springer, Berlin, Heidelberg
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