Graph-Based Recommendation for Sparse and Heterogeneous User Interactions

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Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses struggle to apply state-of-the-art models due to their very limited availability of data. We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets. A genetic algorithm is used to find optimal weights that represent the strength of the relationship between users and content. Experiments on two real-world datasets (which we make available to the research community) show promising results (up to 7 % improvement), in comparison with other state-of-the-art methods for low-data environments. These improvements are statistically significant and consistent across different data samples.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings
EditorsJaap Kamps, Lorraine Goeuriot, Fabio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Annalina Caputo, Udo Kruschwitz
Publication date2023
ISBN (Print)9783031282430
Publication statusPublished - 2023
Event45th European Conference on Information Retrieval, ECIR 2023 - Dublin, Ireland
Duration: 2 Apr 20236 Apr 2023


Conference45th European Conference on Information Retrieval, ECIR 2023
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13980 LNCS

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

    Research areas

  • Collaborative filtering, Genetic algorithm, Personalized page rank

ID: 343224176