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Parham Moradi

Parham Moradi

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 654
Faculty: Faculty of Engineering
Address: Department of Computer Engineering, Faculty of Engineering, University of Kurdistan
Phone:

Research

Title
A pattern mining approach to enhancing the accuracy of collaborative filtering in sparse data domains
Type
JournalPaper
Keywords
Recommender Systems, Collaborative Filtering, Clustering Algorithm, Similarity Measure, Sparse Data.
Year
2014
Journal PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
DOI
Researchers Fardin Akhlaghian Tab ، Parham Moradi ، Mohsen Ramezani

Abstract

Recommender systems seek to find the interesting items by filtering out the worthless items. Collaborative filtering 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 those items which are welcomed by others in the group to the user. But many challenges like sparsity and computational issues are still arising. In this paper, to overcome these challenges, we propose a novel method to find the neighbor users based on the users’ interest patterns. The main idea is that the users who are interested in the same set of items share similar interest patterns. Therefore, the non-redundant item subspaces are extracted to indicate the different patterns of interest. Then a user’s tree structure is created based on the patterns he has in common with the active user. Moreover, a novel recommendation method is presented to predict a new rating value for unseen items. Experimental results on the Movielens and the Jester datasets show that in most cases, the proposed method gains better results than already widely used methods.