Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5220/0008070900830088 | KDIR: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 1: KDIR |
Keywords | Field | DocType |
Recommender Systems, Semantic Web, Collaborative Filtering, Matrix Factorization | Recommender system,Black box (phreaking),Collaborative filtering,Computer science,Matrix decomposition,Word error rate,Semantic Web,Artificial intelligence,Machine learning,Sparse matrix | Conference |
Volume | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Alshammari | 1 | 0 | 0.68 |
| Olfa Nasraoui | 2 | 1515 | 164.53 |