Welcome to Project Solutions !

Project Solutions is the leading final year engineering project providers for IT and Computer Science students across India

An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups

NameAn Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups
Categorydata mining

Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach.

is ieee
ieee paper year2012
price rangehigh
Share this google icon

Get a Call Back

related projects

more projects+


Total 0 comments.
    view all

Post your comment

(It will not be published)