An entity graph based recommender system

Sneha Chaudhari, Amos Azaria, Tom Mitchell

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2016
Externally publishedYes
Event10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States
Duration: 15 Sep 201619 Sep 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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