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

Elnaz Meydani*, Christoph Düsing, Matthias Trier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationInnovation Through Information Systems : Volume II: A Collection of Latest Research on Technology Issues
EditorsFrederik Ahlemann, Reinhard Schütte, Stefan Stieglitz
Number of pages6
Place of PublicationCham
Publication date2021
ISBN (Print)9783030867966
ISBN (Electronic)9783030867973
Publication statusPublished - 2021
Event16th International Conference on Wirtschaftsinformatik. WI 2021 - Virtual Conference, Germany
Duration: 9 Mar 202111 Mar 2021
Conference number: 16


Conference16th International Conference on Wirtschaftsinformatik. WI 2021
LocationVirtual Conference
Internet address
SeriesLecture Notes in Information Systems and Organisation


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

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