Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105651
Title: | Learning user preference from heterogeneous information for store-type recommendation | Authors: | Chen, Y Zhang, J Guo, M Cao, J |
Issue Date: | Nov-2020 | Source: | IEEE transactions on services computing, Nov.-Dec. 2020, v. 13, no. 6, p. 1100-1114 | Abstract: | Online stores are capable analyzing user preference from click logs and transaction records, while retailers of physical stores still lack effective methods to understand user preference. Traditional ways are predominantly field surveys, which are not effective as they need labor-intensive survey thus limit to small populations. As mobile devices and social media are becoming more and more pervasive, user-generated heterogeneous information (e.g., check-in activities and textual reviews) from these platforms are providing rich information to in-depth understand user preference. In this paper, we present a recommendation model for physical stores by learning user's preference from user-generated heterogeneous information. Specifically, the proposed model consists of two phases: 1) offline modeling multi-relation among users, stores and aspects; 2) online graph-based recommendation. The offline modeling phase is designed to learn two kinds of relations: User-Store relation and Store-Aspect relation, while the online recommendation phase automatically produces top-k recommended stores based on the learnt relations with a graph-based model. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets, which are collected by more than 120,000 users during 12 months. Experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness. | Keywords: | Check-in activity Heterogeneous information Store-Type recommendation Textual reviews User preference |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on services computing | EISSN: | 1939-1374 | DOI: | 10.1109/TSC.2017.2755009 | Rights: | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication Y. Chen, J. Zhang, M. Guo and J. Cao, "Learning User Preference from Heterogeneous Information for Store-Type Recommendation," in IEEE Transactions on Services Computing, vol. 13, no. 6, pp. 1100-1114, 1 Nov.-Dec. 2020 is available at https://doi.org/10.1109/TSC.2017.2755009. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Cao_Learning_User_Preference.pdf | Pre-Published version | 2.36 MB | Adobe PDF | View/Open |
Page views
22
Citations as of Jun 30, 2024
Downloads
13
Citations as of Jun 30, 2024
SCOPUSTM
Citations
13
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
12
Citations as of Jul 4, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.