Geodesic
Towards Addressing Item Cold-Start Problem in Collaborative Filtering by Embedding Agglomerative Clustering and FP-Growth into the Recommendation System
Computer Science and Information Systems, Tome 20 (2023) no. 4.

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address the items’ cold-start problem. This difficulty occurs when a new item hits the system, and properly handling such a situation is one of the key success factors of any deployment. The article proposes several strategies to combine collaborative and content-based filtering methods with frequent items mining and agglomerative clustering techniques to mitigate the cold-start problem in recommender systems. The experiments evaluated the developed methods against several quality metrics on three benchmark datasets. The conducted study confirmed usefulness of FPRS in providing apt outcomes even for cold items. The presented solution can be integrated with many different approaches and further extended to make up a complete and standalone RS.
Keywords: recommendation system, cold-start problem, frequent pattern mining, quality of recommendations 
@article{CSIS_2023_20_4_a5,
     author = {Eyad Kannout and Micha{\l} Grodzki and Marek Grzegorowski},
     title = {Towards {Addressing} {Item} {Cold-Start} {Problem} in {Collaborative} {Filtering} by {Embedding} {Agglomerative} {Clustering} and {FP-Growth} into the {Recommendation} {System}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {20},
     number = {4},
     year = {2023},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a5/}
}
TY  - JOUR
AU  - Eyad Kannout
AU  - Michał Grodzki
AU  - Marek Grzegorowski
TI  - Towards Addressing Item Cold-Start Problem in Collaborative Filtering by Embedding Agglomerative Clustering and FP-Growth into the Recommendation System
JO  - Computer Science and Information Systems
PY  - 2023
VL  - 20
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a5/
ID  - CSIS_2023_20_4_a5
ER  - 
%0 Journal Article
%A Eyad Kannout
%A Michał Grodzki
%A Marek Grzegorowski
%T Towards Addressing Item Cold-Start Problem in Collaborative Filtering by Embedding Agglomerative Clustering and FP-Growth into the Recommendation System
%J Computer Science and Information Systems
%D 2023
%V 20
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a5/
%F CSIS_2023_20_4_a5
Eyad Kannout; Michał Grodzki; Marek Grzegorowski. Towards Addressing Item Cold-Start Problem in Collaborative Filtering by Embedding Agglomerative Clustering and FP-Growth into the Recommendation System. Computer Science and Information Systems, Tome 20 (2023) no. 4. http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a5/