[1U4-IS-1a] Knowledge engineering

[1U4-IS-1a-05] Customer Clustering and Customer-Specific Product Recommendation by Distance from Core Customer Group in a Multidimensional Space of Purchase Behavioral Features

〇Kazuhiro Koike^1,2, Daishi Sagawa², Kenji Tanaka² (1. ASKUL Corporation, 2. Univ. of Tokyo)

[[Online, Regular]]

Keywords:Recommender System, Clustering Algorithm, Collaborative Filtering

Recommending attractive products is generally implemented in internet shopping to increase customers’ purchasing desire. When conducting such sales promotion measures, rather than treating all customers equally, a higher degree of effectiveness can be expected when grouping customers by industry sector and their purchasing behavior features and then implementing sales promotions tailored to each group. Furthermore, customizing the promotion for each customer will improve the effectiveness of the measures even further. The method proposed in this study first extracts a core group of customers for each industry sector and clusters other customers according to their distance from the core group in a multidimensional space of dozens of purchase behavior feature variables such as purchase amount, frequency, and the number of categories. Collaborative filtering is then performed within each customer cluster to recommend products suitable for each customer. We confirmed that the proposed method improves LTV by recommending specialized products to customers in clusters with low LTV.