Skip navigation
Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/31988
Title: Probabilistic Neighborhood Selection in Collaborative Filtering Systems
Authors: Adamopoulos, Panagiotis
Tuzhilin, Alexander
Issue Date: 8-Oct-2013
Series/Report no.: ;CBA-13-04
Abstract: This paper presents a novel probabilistic method for recommending items in the neighborhood-based collaborative filtering framework. For the probabilistic neighborhood selection phase, we use an efficient method for weighted sampling of k neighbors without replacement that also takes into consideration the similarity levels between the target user and the candidate neighbors. We conduct an empirical study showing that the proposed method alleviates the over-specialization and concentration biases in common recommender systems by generating recommendation lists that are very different from the classical collaborative filtering approach and also increasing the aggregate diversity and mobility of recommendations. We also demonstrate that the proposed method outperforms both the previously proposed user based k-nearest neighbors and k-furthest neighbors collaborative filtering approaches in terms of item prediction accuracy and utility based ranking measures across various experimental settings. This accuracy performance improvement is in accordance with ensemble learning theory.
URI: http://hdl.handle.net/2451/31988
Appears in Collections:Center for Business Analytics Working Papers

Files in This Item:
File Description SizeFormat 
Probabilistic Neighborhood Selection in Collaborative Filtering Systems.pdf823.98 kBAdobe PDFView/Open


Items in FDA are protected by copyright, with all rights reserved, unless otherwise indicated.