Abstract
In some E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Amazon within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms cannot effectively resolve the situation with a life cycle. In this paper, we model users’ behavior with life cycles by employing hand-crafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user’s long-term behavior using the item taxonomy, and then identify the exact stage of this user. By incorporating collaborative filtering into our method, we can easily provide a personalized item list to this user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Amazon demonstrates the efficacy of our proposed method.
The project was supported in part by the Natural Science Foundation of Fujian Province of China (No.2011J05157).
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Li, L., Hong, W., Li, T. (2012). Taxonomy-Oriented Recommendation towards Recommendation with Stage. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_19
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DOI: https://doi.org/10.1007/978-3-642-29253-8_19
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