34 International Journal of Business Analytics, 1(4), 34-50, October-December 2014 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. ABSTRACT The task of a recommender system evaluation has often been addressed in the literature, however there exists no consensus regarding the best metrics to assess its performance. This research deals with collaborative fltering recommendation systems, and proposes a new approach for evaluating the quality of neighbor selec- tion. It theorizes that good recommendations emerge from good selection of neighbors. Hence, measuring the quality of the neighborhood may be used to predict the recommendation success. Since user neighborhoods in recommender systems are often sparse and differ in their rating range, this paper designs a novel measure to asses a neighborhood quality. First it builds the realization based entropy (RBE), which presents the classical entropy measure from a different angle. Next it modifes the RBE and propose the realization based distance entropy (RBDE), which considers also continuous data. Using the RBDE, it fnally develops the consent en- tropy, which takes into account the absence of rating data. The paper compares the proposed approach with common approaches from the literature, using several recommendation evaluation metrics. It presents offine experiments using the Netfix database. The experimental results confrm that consent entropy performs better than commonly used metrics, particularly with high sparsity neighborhoods. This research is supported by The Israel Science Foundation, Grant #1362/10. This research is supported by NHECD EC, Grant #218639. Neighborhood Evaluation in Recommender Systems Using the Realization Based Entropy Approach Roee Anuar, Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel Yossi Bukchin, Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel Oded Maimon, Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel Lior Rokach, Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel Keywords: Collaborative Filtering, Entropy, Information Systems, Information Theory, Recommender Systems DOI: 10.4018/ijban.2014100103