DOI: 10.5176/978-981-08-7466-7_kd-20
Authors: Tran Nguyen Minh Thu, HO Tuong Vinh, Francois Sempe and Jean Daniel Zucker
Abstract:
Among difficulties encountered by modern shopping recommenders one of the most important is the long tail shape of sold items which is also related to cold-start issues. Various approaches including content-based recommendations attempt to overcome this problem which has a serious impact on the accuracy of recommendations especially when new products are continuously added to the catalogue. A hybrid approach that combines recommendations based on highly correlated pairs of concrete items (existing items) with abstract items (based on the item taxonomy) is proposed in this paper (Abstract-Concrete Recommendation- ACReco). The ACReco algorithm is evaluated on an in-house and a benchmark of 0-1 Data (the Ta Feng data). "Given-n" protocols experimental results shown significant improvements in both the recommendation accuracy and the recommendation of products in the long tail. The overall efficiency of the approach relies on the available Product Taxonomy.
Keywords: Top-N Recommendation System, AssociationRule, Abstraction
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