Abstract
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 language | English |
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Title of host publication | Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings |
Editors | Jaap Kamps, Lorraine Goeuriot, Fabio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Annalina Caputo, Udo Kruschwitz |
Publisher | Springer |
Publication date | 2023 |
Pages | 182-199 |
ISBN (Print) | 9783031282430 |
DOIs | |
Publication status | Published - 2023 |
Event | 45th European Conference on Information Retrieval, ECIR 2023 - Dublin, Ireland Duration: 2 Apr 2023 → 6 Apr 2023 |
Conference
Conference | 45th European Conference on Information Retrieval, ECIR 2023 |
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Country/Territory | Ireland |
City | Dublin |
Period | 02/04/2023 → 06/04/2023 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13980 LNCS |
ISSN | 0302-9743 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Collaborative filtering
- Genetic algorithm
- Personalized page rank