Sequential Recommendation Based on Objective and Subjective Features

Authors

Wenhao Guo, college of management and economicFollow
Jin Tian, College of Management and Economics, Tianjin UniversityFollow
Minqiang Li, College of Management and Economics, Tianjin UniversityCollege of Management and Economics, Tianjin UniversityCollege of Management and Economics, Tianjin UniversityFollow

Abstract

Nowadays, sequential recommender systems are widely used in E-commerce fields to capture consumers’ dynamic preferences in short terms. Existing transformer-based recommendation models mainly consider consumer preference for the products and some related features, such as price. However, besides such objective features, some subjective features, such as consumers’ preference for product quality, also affect consumers’ purchase decisions. In this paper, we design a Sequential Recommender system based on Objective and Subjective features (SROS). We construct subjective features by using natural language processing to analyze online consumer reviews. Then we design a feature-level multi-head self-attention to explore the interactions between objective features and subjective features and capture consumers’ dynamic preferences for them among different purchases. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model.

Comments

Paper Number 1469; Track AI; Short Paper

Recommended Citation

Guo, Wenhao; Tian, Jin; and Li, Minqiang, "Sequential Recommendation Based on Objective and Subjective Features" (2023). PACIS 2023 Proceedings. 114.
https://aisel.aisnet.org/pacis2023/114