Mining longitudinal user sessions with deep learning to extend the boundary of consumer priming
作者：
Highlights：
• The proposed framework using NLP and deep learning techniques approximate user heuristics after B&B product shortlisting.
• The proposed framework was an extension of the priming theory into product comparison and shortlisting stages that were traditionally difficult for marketers to tap into.
• This study proposed a recommendation system built based on the learned consumer heuristics to recommend B&B options.
• We collaborated with an online accommodation booking platforms company to analyze their consumer data for this research.
摘要
•The proposed framework using NLP and deep learning techniques approximate user heuristics after B&B product shortlisting.•The proposed framework was an extension of the priming theory into product comparison and shortlisting stages that were traditionally difficult for marketers to tap into.•This study proposed a recommendation system built based on the learned consumer heuristics to recommend B&B options.•We collaborated with an online accommodation booking platforms company to analyze their consumer data for this research.
论文关键词：Text mining,Session-based recommendation,Priming theory,E-commerce
论文评审过程：Received 29 July 2021, Revised 13 July 2022, Accepted 16 August 2022, Available online 6 September 2022, Version of Record 20 September 2022.
论文官网地址：https://doi.org/10.1016/j.dss.2022.113864
