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1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
IMPROVING COLLABORATIVE RECOMMENDATION VIA USER-ITEM
SUBGROUPS
ABSTRACT:
Collaborative filtering (CF) is out of question the most widely adopted and
successful recommendation approach. A typical CF-based recommender
system associates a user with a group of like-minded users based on their
individual preferences over all the items, either explicit or implicit, and then
recommends to the user some unobserved items enjoyed by 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 reasonable to predict preferences
through one user’s correlated subgroups, but not the entire user-item matrix.
In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-
Clustering (MCoC) model, which captures relations of user-to-item, user-to-
user, and item-to-item simultaneously. Then we combine traditional CF
algorithms with subgroups for improving their top-N recommendation
performance. Our approach can be seen as a new extension of traditional
clustering CF models. Systematic experiments on several real data sets have
demonstrated the effectiveness of our proposed approach

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
CONCLUSIONS
In this paper, we explore a new improving space for collaborative
recommender systems – utilizing user item subgroups, which is helpful to
capture similar user tastes on a subset of items. We propose to solve an
extended Multiclass Co-Clustering problem to find subgroups. It is a natural
extension of traditional clustering CF models. Our method models the userto-
user, user-to-item, and item-to-item relations simultaneously into a unified
optimization problem and adopt an approximate solution. Experimental results
show that using subgroups is a promising way to further improve the top-N
recommendation performance for many popular CF methods. We expect that
our exploration can attract further research or practice on the topic of
clustering CF model. Future works are needed in two main aspects: one is to
find better user-item subgroups and the other is to design new methods to
fully utilize subgroups. More information can be involved in our framework,
e.g., the user relationship and item tags, to generate more informative
subgroups.
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3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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4. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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