TY - JOUR
T1 - A collaborative filtering probabilistic approach for recommendation to large homogeneous and automatically detected groups
AU - Hurtado, Remigio
AU - Bobadilla, Jesús
AU - Gutiérrez, Abraham
AU - Alonso, Santiago
N1 - Publisher Copyright:
© 2020, Universidad Internacional de la Rioja. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Collaborative Filtering Clustering
KW - Dimensionality Reduction
KW - Group Recommendation
KW - Homogenous Groups
KW - Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=85175101911&partnerID=8YFLogxK
U2 - 10.9781/ijimai.2020.03.002
DO - 10.9781/ijimai.2020.03.002
M3 - Article
AN - SCOPUS:85175101911
SN - 1989-1660
VL - 6
SP - 90
EP - 100
JO - International Journal of Interactive Multimedia and Artificial Intelligence
JF - International Journal of Interactive Multimedia and Artificial Intelligence
IS - 2
ER -