Recommendation Based on Co-clustring Algorithm, Co-dissimilarity and Spanning Tree
Ouafae Baida, Najma Hamzaoui, Abdelfettah Sedqui, Abdelouahid Lyhyaoui
International Journal of Computational Linguistics Research, Vol. 3, No. 3, 2012.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v3n3/3.pdf
Abstract:
 Recommender system is a system that helps users to find interesting items. Actually, collaborative filtering technology is one of the most successful techniques in recommender system. In this article we propose two new approaches based on the co-clustering and co-dissimilarity between users. In the literature, we find a lot of approaches able to recommend items to the user. Aiming to offer a list of interesting items, we use a hybrid approach of collaborative filtering that perfo rms better than others. Our collaborative filtering approach is partitioned in two steps, the first based a bond energy algorithm (BEA), itâ€™s one of group technology algorithm, its objective is realized the co-clustering or simultaneous clustering. The second is recommendation based on the graph theory, when we propose the use of kruskal algorithm; this one gives us a spanning tree with minimum weight from a connected graph. We define a group of criteria that help to determine the best items to recommend without computing the rating predictio