Abstract
Session-based recommendation systems aim to model users' interests based on their sequential interactions to predict the next item in an ongoing session. In this work, we present a novel approach that can be used in session-based recommendations (SBRs). Our goal is to enhance the prediction accuracy of an existing session-based recommendation model, the SR-GNN model, by introducing an adaptive weighting mechanism applied to the graph neural network (GNN) vectors. This mechanism is designed to incorporate various types of side information obtained through different methods during the study. Items are assigned varying degrees of importance within each session as a result of the weighting mechanism. We hypothesize that this adaptive weighting strategy will contribute to more accurate predictions and thus improve the overall performance of SBRs in different scenarios. The adaptive weighting strategy can be utilized to address the cold start problem in SBRs by dynamically adjusting the importance of items in each session, thus providing better recommendations in cold start situations, such as for new users or newly added items. Our experimental evaluations on the Dressipi dataset demonstrate the effectiveness of the proposed approach compared to traditional models in enhancing the user experience and highlighting its potential to optimize the recommendation results in real-world applications.
Original language | English |
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Title of host publication | Proceedings of the 2024 9th International Conference on Machine Learning Technologies, ICMLT 2024 |
Publisher | Association for Computing Machinery |
Pages | 258-264 |
Number of pages | 7 |
ISBN (Electronic) | 9798400716379 |
DOIs | |
Publication status | Published - 24 May 2024 |
Event | 9th International Conference on Machine Learning Technologies, ICMLT 2024 - Oslo, Norway Duration: 24 May 2024 → 26 May 2024 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 9th International Conference on Machine Learning Technologies, ICMLT 2024 |
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Country/Territory | Norway |
City | Oslo |
Period | 24/05/24 → 26/05/24 |
Bibliographical note
Publisher Copyright:© 2024 Owner/Author.
Keywords
- adaptive weights
- graph neural network
- next-item recommendation
- SR-GNN