TY - GEN
T1 - Improved Session-based Recommender System by Context Awareness in e-Commerce Domain
AU - Esmeli, Ramazan
AU - Bader-El-Den, Mohamed
AU - Abdullahi, Hassana
AU - Henderson, David
N1 - Publisher Copyright:
Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2021/11/2
Y1 - 2021/11/2
N2 - Over the past two decades, there has been a rapid increase in the number of online sales. This has resulted in an increase in the data collected about the users' behaviour which has aided the development of several novel Recommender System (RS) methods. One of the main problem in RS is the lack of "explicit rating"; many customers do not rate the items they buy or view. However, the user behaviour (e.g. session duration, number of items, item duration view, etc.) during an online session could give an indication about what the user preferences "implicit rating". In this paper, we present a method to derive numerical implicit ratings from user browsing behaviour. Also, we examine the impact of using the derived numerical implicit ratings as context factors on some of the main RS methods, i.e. Factorisation Recommender and Item-Item Collaborative Filtering models. We evaluate the performance of the proposed framework on two large e-commerce datasets. The computational experiments show that in the absence of user explicit rating, the use of the user behaviour data to generate numerical implicit ratings could significantly improve the several RS methods.
AB - Over the past two decades, there has been a rapid increase in the number of online sales. This has resulted in an increase in the data collected about the users' behaviour which has aided the development of several novel Recommender System (RS) methods. One of the main problem in RS is the lack of "explicit rating"; many customers do not rate the items they buy or view. However, the user behaviour (e.g. session duration, number of items, item duration view, etc.) during an online session could give an indication about what the user preferences "implicit rating". In this paper, we present a method to derive numerical implicit ratings from user browsing behaviour. Also, we examine the impact of using the derived numerical implicit ratings as context factors on some of the main RS methods, i.e. Factorisation Recommender and Item-Item Collaborative Filtering models. We evaluate the performance of the proposed framework on two large e-commerce datasets. The computational experiments show that in the absence of user explicit rating, the use of the user behaviour data to generate numerical implicit ratings could significantly improve the several RS methods.
KW - Context Awareness
KW - Recommender Systems
KW - User Behaviour Modelling
KW - e-Commerce
UR - http://www.scopus.com/inward/record.url?scp=85146199235&partnerID=8YFLogxK
U2 - 10.5220/0010656100003064
DO - 10.5220/0010656100003064
M3 - Conference contribution
AN - SCOPUS:85146199235
T3 - International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings
SP - 37
EP - 47
BT - 13th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2021 as part of IC3K 2021 - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
A2 - Cucchiara, Rita
A2 - Fred, Ana
A2 - Filipe, Joaquim
PB - Science and Technology Publications, Lda
T2 - 13th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2021 as part of 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021
Y2 - 25 October 2022 through 27 October 2022
ER -