02014nas a2200253   4500000000100000000000100001008004100002260001200043653002700055653001500082653002800097653002900125653002500154100002100179700002300200700002000223700002000243245012700263856008100390300000700471490000600478520126200484022001401746       2020                            d  c06/202010aRecommendation Systems10aClustering10aCollaborative Filtering10aDimensionality Reduction10aGroup Recommendation1 aJesús Bobadilla1 aAbraham Gutiérrez1 aSantiago Alonso1 aRemigio Hurtado00aA Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups  uhttps://www.ijimai.org/journal/sites/default/files/2020-05/ijimai_6_2_10.pdf  a110 v63 aIn 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.  a1989-1660