We propose a recommender system which 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.
|Journal||CEUR Workshop Proceedings|
|State||Published - 2016|
|Event||10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States|
Duration: 15 Sep 2016 → 19 Sep 2016
All Science Journal Classification (ASJC) codes
- Computer Science(all)