Name
Abstract | ||
|---|---|---|
Recommender Systems have become increasingly important and are applied in an increasing number of domains. While common collaborative methods measure similarity between different users, common content based methods measure similarity between different content. We propose a privacy aware recommender system that exploits relations present between entities appearing in content from user's history and entities appearing in candidate content. In order to identify such relations, we use the knowledge graph of NELL, which encodes entities and their relations. We present a novel normalized version of Personalized PageRank, to rank candidate content. We test our approach on the movie recommendation domain and show that the proposed method outperforms other baseline methods, including the standard Personalized PageRank. We intend to deploy our recommender system as a news recommendation app for mobile devices. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/AIC-170728 | AI COMMUNICATIONS |
Keywords | DocType | Volume |
Recommender Systems,knowledge-graphs,PageRank | Journal | 30 |
Issue | ISSN | Citations |
2 | 0921-7126 | 5 |
PageRank | References | Authors |
0.59 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sneha Chaudhari | 1 | 6 | 1.57 |
| Amos Azaria | 2 | 272 | 32.02 |
| Tom M. Mitchell | 3 | 7160 | 1946.42 |