Predicting Online Item-Choice Behavior: A Shape-Restricted Regression Approach

This paper examines the relationship between user pageview (PV) histories and their itemchoice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user–item pair. We propose a shape-restricted optimization model that accurately estim...

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Bibliographic Details
Published inAlgorithms Vol. 16; no. 9; p. 415
Main Authors Nishimura, Naoki, Sukegawa, Noriyoshi, Takano, Yuichi, Iwanaga, Jiro
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Algorithms
Constraint modelling
Deep learning
e-commerce
Electronic commerce
item choice
Machine learning
Neural networks
Optimization
Optimization models
partially ordered set
Probability
Recommender systems
Regression analysis
Sequences
Support vector machines
User behavior
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Summary:This paper examines the relationship between user pageview (PV) histories and their itemchoice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user–item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user’s previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods.
ISSN:1999-4893
1999-4893
DOI:10.3390/a16090415