Standard
Graph-Based Recommendation for Sparse and Heterogeneous User Interactions. / Bruun, Simone Borg; Leśniak, Kacper Kenji; Biasini, Mirko; Carmignani, Vittorio; Filianos, Panagiotis; Lioma, Christina; Maistro, Maria.
Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings. ed. / Jaap Kamps; Lorraine Goeuriot; Fabio Crestani; Maria Maistro; Hideo Joho; Brian Davis; Cathal Gurrin; Annalina Caputo; Udo Kruschwitz. Springer, 2023. p. 182-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13980 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Bruun, SB, Leśniak, KK, Biasini, M, Carmignani, V, Filianos, P
, Lioma, C & Maistro, M 2023,
Graph-Based Recommendation for Sparse and Heterogeneous User Interactions. in J Kamps, L Goeuriot, F Crestani, M Maistro, H Joho, B Davis, C Gurrin, A Caputo & U Kruschwitz (eds),
Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13980 LNCS, pp. 182-199, 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland,
02/04/2023.
https://doi.org/10.1007/978-3-031-28244-7_12
APA
Bruun, S. B., Leśniak, K. K., Biasini, M., Carmignani, V., Filianos, P.
, Lioma, C., & Maistro, M. (2023).
Graph-Based Recommendation for Sparse and Heterogeneous User Interactions. In J. Kamps, L. Goeuriot, F. Crestani, M. Maistro, H. Joho, B. Davis, C. Gurrin, A. Caputo, & U. Kruschwitz (Eds.),
Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings (pp. 182-199). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13980 LNCS
https://doi.org/10.1007/978-3-031-28244-7_12
Vancouver
Bruun SB, Leśniak KK, Biasini M, Carmignani V, Filianos P
, Lioma C et al.
Graph-Based Recommendation for Sparse and Heterogeneous User Interactions. In Kamps J, Goeuriot L, Crestani F, Maistro M, Joho H, Davis B, Gurrin C, Caputo A, Kruschwitz U, editors, Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings. Springer. 2023. p. 182-199. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13980 LNCS).
https://doi.org/10.1007/978-3-031-28244-7_12
Author
Bruun, Simone Borg ; Leśniak, Kacper Kenji ; Biasini, Mirko ; Carmignani, Vittorio ; Filianos, Panagiotis ; Lioma, Christina ; Maistro, Maria. / Graph-Based Recommendation for Sparse and Heterogeneous User Interactions. Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings. editor / Jaap Kamps ; Lorraine Goeuriot ; Fabio Crestani ; Maria Maistro ; Hideo Joho ; Brian Davis ; Cathal Gurrin ; Annalina Caputo ; Udo Kruschwitz. Springer, 2023. pp. 182-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13980 LNCS).
Bibtex
@inproceedings{1a2265e9adfb4497a20fdbf512489421,
title = "Graph-Based Recommendation for Sparse and Heterogeneous User Interactions",
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.",
keywords = "Collaborative filtering, Genetic algorithm, Personalized page rank",
author = "Bruun, {Simone Borg} and Le{\'s}niak, {Kacper Kenji} and Mirko Biasini and Vittorio Carmignani and Panagiotis Filianos and Christina Lioma and Maria Maistro",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 45th European Conference on Information Retrieval, ECIR 2023 ; Conference date: 02-04-2023 Through 06-04-2023",
year = "2023",
doi = "10.1007/978-3-031-28244-7_12",
language = "English",
isbn = "9783031282430",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "182--199",
editor = "Jaap Kamps and Lorraine Goeuriot and Fabio Crestani and Maria Maistro and Hideo Joho and Brian Davis and Cathal Gurrin and Annalina Caputo and Udo Kruschwitz",
booktitle = "Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Graph-Based Recommendation for Sparse and Heterogeneous User Interactions
AU - Bruun, Simone Borg
AU - Leśniak, Kacper Kenji
AU - Biasini, Mirko
AU - Carmignani, Vittorio
AU - Filianos, Panagiotis
AU - Lioma, Christina
AU - Maistro, Maria
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Genetic algorithm
KW - Personalized page rank
UR - http://www.scopus.com/inward/record.url?scp=85151140417&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-28244-7_12
DO - 10.1007/978-3-031-28244-7_12
M3 - Article in proceedings
AN - SCOPUS:85151140417
SN - 9783031282430
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 182
EP - 199
BT - Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings
A2 - Kamps, Jaap
A2 - Goeuriot, Lorraine
A2 - Crestani, Fabio
A2 - Maistro, Maria
A2 - Joho, Hideo
A2 - Davis, Brian
A2 - Gurrin, Cathal
A2 - Caputo, Annalina
A2 - Kruschwitz, Udo
PB - Springer
T2 - 45th European Conference on Information Retrieval, ECIR 2023
Y2 - 2 April 2023 through 6 April 2023
ER -
