{"title":"Dynamic Context in Graph Neural Networks for Item Recommendation","authors":"Asma Sattar, D. Bacciu","doi":"10.1109/SSCI50451.2021.9659550","DOIUrl":null,"url":null,"abstract":"Graph neural networks allow to build recommendation systems which can straightforwardly take into account relational knowledge concerning multiple types of interactions, such as user-item relationships, but also interactions between users and within items. Graph-based approaches in the literature consider such interactions to be static, independent of the surroundings. In this paper, we put forward a novel approach to graph-based item recommendation built on the foundational idea that relational knowledge is characterized by a dynamic nature of the user and its surroundings. We claim that being able to capture such dynamic knowledge allows to build richer contexts upon which more precise recommendations can be built, e.g., taking into account current location, weather conditions, and user mood. The paper provides recipes to build and integrate dynamic user and item contexts in existing item recommendation tasks. We also introduce a novel Dynamic Context-aware Graph Neural Network (DCGNN) that can effectively leverage the knowledge of surroundings to learn the context-aware recommendation behaviour of users. The empirical analysis shows how our model outperforms static state-of-the-art approaches on four movie and travel recommendation benchmarks.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSCI50451.2021.9659550","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Graph neural networks allow to build recommendation systems which can straightforwardly take into account relational knowledge concerning multiple types of interactions, such as user-item relationships, but also interactions between users and within items. Graph-based approaches in the literature consider such interactions to be static, independent of the surroundings. In this paper, we put forward a novel approach to graph-based item recommendation built on the foundational idea that relational knowledge is characterized by a dynamic nature of the user and its surroundings. We claim that being able to capture such dynamic knowledge allows to build richer contexts upon which more precise recommendations can be built, e.g., taking into account current location, weather conditions, and user mood. The paper provides recipes to build and integrate dynamic user and item contexts in existing item recommendation tasks. We also introduce a novel Dynamic Context-aware Graph Neural Network (DCGNN) that can effectively leverage the knowledge of surroundings to learn the context-aware recommendation behaviour of users. The empirical analysis shows how our model outperforms static state-of-the-art approaches on four movie and travel recommendation benchmarks.