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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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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 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.
College: Faculty of Science and Engineering
Start Page: 1321
End Page: 1326

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