Automatically detection and recommendation in collaborative groups

Recommender Systems (RSs) are information filtering procedures used for delivering suggestions to the user based on either user's individual taste or are centered on the resemblance of a user to another user. In this paper, the focus has been on group recommendations based on collaborative filtering method incorporated with various group-modeling strategies[1]. These group-modeling strategies combine various user model into a single model and represent the available knowledge about user preference belonging to a group. The users are clustered into groups according to their ratings using a variant of k-means clustering algorithm EZ Hybrid[2]. After clustering the users and group formation the group ratings are predicted for all the items and finally, the prediction accuracy is tested for the different strategies. Additive utilitarian, Approval voting with threshold 1 and 2, least misery and most pleasure strategy from existing literature were implemented. A new group modeling strategy “Median strategy” is proposed and its performance is compared with those present in the literature. 1M dataset from Movielens is used in the experiment. RMSE, MAE, Precision, and Recall are the parameters used to measure the performance of prediction accuracy for autodetected groups. From results, we conclude that the new proposed strategy gives better Precision, Recall, and MAE compared to already present in literature. MAE is improved by 6.67%, precision by 7.77% and recall by 9.69%. In addition, the RMSE values are better than all other strategies except additive utilitarian. Hence, we conclude that using median strategy helps in making predictions that are more accurate.