Authors:
Mohammed Alshammari
1
and
Olfa Nasraoui
2
Affiliations:
1
Knowledge Discovery and Web Mining Lab, CECS Department, University of Louisville, Louisville, Kentucky 40292, U.S.A., Northern Border University, Rafha 76313 and Saudi Arabia
;
2
Knowledge Discovery and Web Mining Lab, CECS Department, University of Louisville, Louisville, Kentucky 40292 and U.S.A.
Keyword(s):
Recommender Systems, Semantic Web, Collaborative Filtering, Matrix Factorization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaborative Filtering
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the rating for unseen items with high accuracy. However, they fail to justify their output. The main objective of this paper is to present a novel approach that employs Semantic Web technologies to generate explanations for the output of black box recommender systems. The proposed model significantly outperforms state-of-the-art baseline models in terms of the error rate. Moreover, it produces more explainable items than all baseline approaches.