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Conference Paper/Proceeding/Abstract 383 views

Decision-Behavior Based Online Shopping

Gongqi Lin, Yuan Miao, Siyuan Liu Orcid Logo

Pages: 1321 - 1326

Swansea University Author: Siyuan Liu Orcid Logo

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DOI (Published version): 10.1109/ICARCV.2018.8581336

Abstract

The explosive popularity of e-commerce sites has reshaped users' shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to collect rich data about users. The majority of traditional recommender systems have focused on...

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ISBN: 978-1-5386-9583-8 978-1-5386-9582-1
Published: Singapore 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) 2018
Online Access: https://icarcv.net/
URI: https://cronfa.swan.ac.uk/Record/cronfa51996
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first_indexed 2019-09-23T14:18:30Z
last_indexed 2019-10-04T15:09:41Z
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spelling 2019-10-04T09:39:17.3683653 v2 51996 2019-09-23 Decision-Behavior Based Online Shopping 65eb72224498b541c31d702329a2a9d5 0000-0003-1121-594X Siyuan Liu Siyuan Liu true false 2019-09-23 SCS The explosive popularity of e-commerce sites has reshaped users' shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to collect rich data about users. The majority of traditional recommender systems have focused on the macro interactions between users and items, particularly the purchase history of customers. However, decision support only achieved limited performance due to the mismatch between the causality and the interaction sequence. It is especially challenging for products with low purchase frequency, such as refrigerators, or new users with little history data. To address the problem, we investigated how to leverage the heterogenous information, including decision making information to improve recommender systems, helping users approach their items easier and more accurately. Specifically, we propose to model users' purchasing reason information and knowledge graph of items to provide personalized recommendation. The new recommend model, called Decision-Behavior Knowledge Graph (DBKG), captures the decision-making knowledge during users' online purchasing, and update the decision making knowledge in the process of supporting users' purchase decision. Conference Paper/Proceeding/Abstract 1321 1326 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Singapore 978-1-5386-9583-8 978-1-5386-9582-1 20 12 2018 2018-12-20 10.1109/ICARCV.2018.8581336 https://icarcv.net/ COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-10-04T09:39:17.3683653 2019-09-23T10:59:14.8734343 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Gongqi Lin 1 Yuan Miao 2 Siyuan Liu 0000-0003-1121-594X 3
title Decision-Behavior Based Online Shopping
spellingShingle Decision-Behavior Based Online Shopping
Siyuan Liu
title_short Decision-Behavior Based Online Shopping
title_full Decision-Behavior Based Online Shopping
title_fullStr Decision-Behavior Based Online Shopping
title_full_unstemmed Decision-Behavior Based Online Shopping
title_sort Decision-Behavior Based Online Shopping
author_id_str_mv 65eb72224498b541c31d702329a2a9d5
author_id_fullname_str_mv 65eb72224498b541c31d702329a2a9d5_***_Siyuan Liu
author Siyuan Liu
author2 Gongqi Lin
Yuan Miao
Siyuan Liu
format Conference Paper/Proceeding/Abstract
container_start_page 1321
publishDate 2018
institution Swansea University
isbn 978-1-5386-9583-8
978-1-5386-9582-1
doi_str_mv 10.1109/ICARCV.2018.8581336
publisher 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url https://icarcv.net/
document_store_str 0
active_str 0
description The explosive popularity of e-commerce sites has reshaped users' shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to collect rich data about users. The majority of traditional recommender systems have focused on the macro interactions between users and items, particularly the purchase history of customers. However, decision support only achieved limited performance due to the mismatch between the causality and the interaction sequence. It is especially challenging for products with low purchase frequency, such as refrigerators, or new users with little history data. To address the problem, we investigated how to leverage the heterogenous information, including decision making information to improve recommender systems, helping users approach their items easier and more accurately. Specifically, we propose to model users' purchasing reason information and knowledge graph of items to provide personalized recommendation. The new recommend model, called Decision-Behavior Knowledge Graph (DBKG), captures the decision-making knowledge during users' online purchasing, and update the decision making knowledge in the process of supporting users' purchase decision.
published_date 2018-12-20T04:04:06Z
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score 11.012678

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