A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

TitleA Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsHurtado, R., J. Bobadilla, A. Gutiérrez, and S. Alonso
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueIn Press
VolumeIn Press
NumberIn Press
Date Published03/2020
Pagination1-11
Abstract

In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.

KeywordsClustering, Collaborative Filtering, Dimensionality Reduction, Group Recommendation, Recommendation Systems
DOI10.9781/ijimai.2020.03.002
URLhttps://www.ijimai.org/journal/sites/default/files/files/2020/03/ip2020_03_02_pdf_53155.pdf
AttachmentSize
ip2020_03_02.pdf3.62 MB