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Advances in Artificial Intelligence
Volume 2016 (2016), Article ID 9386368, 10 pages
Research Article

Effect of Collaborative Recommender System Parameters: Common Set Cardinality and the Similarity Measure

1Computer Engineering Department, College of Computer Science, King Khalid University, P.O. Box 394, Abha, Saudi Arabia
2Electrical Engineering Department, Faculty of Engineering and Architecture, Ibb University, Ibb, Yemen

Received 25 March 2016; Revised 13 May 2016; Accepted 22 May 2016

Academic Editor: Theo Van Der Weide

Copyright © 2016 Mohammad Yahya H. Al-Shamri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recommender systems are widespread due to their ability to help Web users surf the Internet in a personalized way. For example, collaborative recommender system is a powerful Web personalization tool for suggesting many useful items to a given user based on opinions collected from his neighbors. Among many, similarity measure is an important factor affecting the performance of the collaborative recommender system. However, the similarity measure itself largely depends on the overlapping between the user profiles. Most of the previous systems are tested on a predefined number of common items and neighbors. However, the system performance may vary if we changed these parameters. The main aim of this paper is to examine the performance of the collaborative recommender system under many similarity measures, common set cardinalities, rating mean groups, and neighborhood set sizes. For this purpose, we propose a modified version for the mean difference weight similarity measure and a new evaluation metric called users’ coverage for measuring the recommender system ability for helping users. The experimental results show that the modified mean difference weight similarity measure outperforms other similarity measures and the collaborative recommender system performance varies by varying its parameters; hence we must specify the system parameters in advance.