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Click-aware Purchase Prediction with Push at the Top

Title
Click-aware Purchase Prediction with Push at the Top
Authors
Chanyoung ParkDonghyun KimMin-Chul YangJung-Tae LeeHwanjo Yu
Date Issued
Jun-2020
Publisher
ELSEVIER SCIENCE INC
Abstract
Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user–item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but- not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.
URI
http://oasis.postech.ac.kr/handle/2014.oak/101443
ISSN
0020-0255
Article Type
Article
Citation
INFORMATION SCIENCES, vol. 521, page. 350 - 364, 2020-06
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 YU, HWANJO
Dept of Computer Science & Enginrg
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