A Comprehensive Solution to Predict Short-term and Long-term user Intention with Environmental Context

Prediction of user intention is an important task in business intelligence and analysis. Our research divides user intention into short-term and long-term, corresponding to the first purchase and repurchase scenarios respectively. To model short-term user consumption intention prediction, we present a comprehensive solution based on extracting user sequence behavior features and computing user different types of interest scores. At the same time, we take environmental context into consideration to explore the occurrence environment of user behavior. To detect long-term intention, we use a combined topic modeling method to extract aspects from user reviews with an unsupervised method. Our research builds a HGNN and RGCN using sentiment polarity, aspects, and items as nodes and edges of the graph neural network. This method entirely considers the close relation between user sentiment polarity change and item features, and the solution shows good performance when compared with the baseline model in the experiments.

Name thesis:degree_name

Master of Information Technology (M.I.T.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Digital Media

Grantor dc:publisher

Carleton University

Year dc:date.issued

2023

Author dc:creator

• Zhang, Hang

Statement dc:rights

• Copyright © 2022 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2023-15412

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/42806