{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/42806"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/42806","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"A Comprehensive Solution to Predict Short-term and Long-term user Intention with Environmental Context","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Zhang, Hang"],"institution":"Carleton University","degree_name":"Master of Information Technology (M.I.T.)","degree_level":"Master's","degree_discipline":"Digital Media","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T01:34:43Z","subjects":[],"languages":["en"],"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. 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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."]},{"key":"dc:title","label":"Title","values":["A Comprehensive Solution to Predict Short-term and Long-term user Intention with Environmental Context"]}]}],"canonical_facts":{"dc:creator":["Zhang, Hang"],"dc:date.accessioned":["2025-04-08T20:44:25Z"],"dc:date.available":["2025-04-08T20:44:25Z"],"dc:date.issued":["2023"],"dc:description.abstract":["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."],"dc:identifier.doi":["10.22215/etd/2023-15412"],"dc:identifier.uri":["https://hdl.handle.net/20.500.14718/42806"],"dc:language.iso":["en"],"dc:publisher":["Carleton University"],"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. 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