Stochastic-control multi-attribute preference learning with sparse reviews

TY - JOUR

T1 - Stochastic-control multi-attribute preference learning with sparse reviews

AU - Zhu, Bin

AU - Xu, Yuanzhen

AU - Luo, Shucheng

N1 - Publisher Copyright: © 2025 Elsevier Ltd

PY - 2025/9/15

Y1 - 2025/9/15

N2 - Analyzing consumer preferences with their reviews, including ratings and text comments, is critical for marketing, recommendation, and other applications. Focusing on a target consumer's preferences represented by multi-attribute weights, the existing approaches face the difficulties when analyzing with sparse rating information. To address this issue, we propose a stochastic control multi-attribute preference learning approach. This approach can utilize both the reviews of the target consumer and reference consumers to learn this target consumer's preferred multi-attribute weights, thereby deals with the sparse issue. Specifically, this approach can efficiently learn the weights using rating information extracted from reviews. This includes a control variable that controls how to utilize other consumers’ reviews that aid the learning, which balances the computational cost and its performance. We verify the effectiveness of our approach with generated data. In addition, with real review data, we apply our approach to a recommendation scenario, and show its advantages compared with some state-of-the-art recommendation methods.

AB - Analyzing consumer preferences with their reviews, including ratings and text comments, is critical for marketing, recommendation, and other applications. Focusing on a target consumer's preferences represented by multi-attribute weights, the existing approaches face the difficulties when analyzing with sparse rating information. To address this issue, we propose a stochastic control multi-attribute preference learning approach. This approach can utilize both the reviews of the target consumer and reference consumers to learn this target consumer's preferred multi-attribute weights, thereby deals with the sparse issue. Specifically, this approach can efficiently learn the weights using rating information extracted from reviews. This includes a control variable that controls how to utilize other consumers’ reviews that aid the learning, which balances the computational cost and its performance. We verify the effectiveness of our approach with generated data. In addition, with real review data, we apply our approach to a recommendation scenario, and show its advantages compared with some state-of-the-art recommendation methods.

KW - Choice decision

KW - Multi-attribute decision making

KW - Online consumer

KW - Preference learning

UR - https://www.scopus.com/pages/publications/105007027788

U2 - 10.1016/j.eswa.2025.128271

DO - 10.1016/j.eswa.2025.128271

M3 - Article

AN - SCOPUS:105007027788

SN - 0957-4174

VL - 289

JO - Expert Systems with Applications

JF - Expert Systems with Applications

M1 - 128271

ER -