Journal Article

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

Sukegawa, Noriyoshi, Takano, Yuichi, Nishimura, Naoki, Iwanaga, Jiro

2023

Overview

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.

• Publisher: MDPI AG
• Subject: 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