Group Recommender Systems: Combining User-User and Item-Item Collaborative Filtering Techniques

Abstract

Recommender Systems, these days, are no longer personal recommender systems, rather they are group recommender systems which list out recommendations for a group of users. Also, they are an integral part of today's web sites (mainly shopping, search engine etc.) who want to keep track of their users' preferences. Although we cannot build a recommender system for every individual, we can build a recommender system which considers the preferences of a group of users. Hence the concept of group recommendation is even more difficult. Recent researches transpire that there is no efficient group recommendation technique available in the market and also the techniques developed till date are good for individual application or web sites. In this paper we have proposed a new approach to group recommender system using collaborative filtering technique which is one of the two techniques of building recommender systems. In our proposed method we have combined the features of item-item collaborative filtering as well as user-user collaborative filtering to make efficient group recommendation by making homogeneous groups. We have also made a sincere attempt to list out the precision of our group recommender system by using the movie lens data set which is mostly used worldwide for recommender system testing.