Towards a Trust-aware Item Recommendation System on a Graph Autoencoder with Attention Mechanism

Elnaz Meydani*, Christoph Düsing, Matthias Trier

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

Recommender Systems provide users with recommendations for potential items of interest in applications like e-commerce and social media. User information such as past item ratings and personal data can be considered as inputs of these systems. In this study, we aim to utilize a trust-graph-based Neural Network in the recommendation process. The proposed method tries to increase the performance of graph-based RSs by considering the inferred level of trust and its evolution. These recommendations will not only be based on the user information itself but will be fueled by information about associates in the network. To improve the system performance, we develop an attention mechanism to infer a level of trust for each connection in the network. As users are likely to be influenced more by those whom they trust the most, our method might lead to more personalized recommendations, which is likely to increase the user experience and satisfaction.
OriginalsprogEngelsk
TitelInnovation Through Information Systems : Volume II: A Collection of Latest Research on Technology Issues
RedaktørerFrederik Ahlemann, Reinhard Schütte, Stefan Stieglitz
Antal sider6
UdgivelsesstedCham
ForlagSpringer
Publikationsdato2021
Sider72-77
ISBN (Trykt)9783030867966
ISBN (Elektronisk)9783030867973
DOI
StatusUdgivet - 2021
Begivenhed16th International Conference on Wirtschaftsinformatik. WI 2021 - Virtual Conference, Tyskland
Varighed: 9 mar. 202111 mar. 2021
Konferencens nummer: 16
https://wi2021.de/start-2.html

Konference

Konference16th International Conference on Wirtschaftsinformatik. WI 2021
Nummer16
LokationVirtual Conference
Land/OmrådeTyskland
Periode09/03/202111/03/2021
Internetadresse
NavnLecture Notes in Information Systems and Organisation
Vol/bind47
ISSN2195-4968

Emneord

  • Recommender systems
  • Trust-aware recommendations
  • Autoencoders
  • Attention mechanism

Citationsformater