TY - JOUR
T1 - An entity graph based recommender system
AU - Chaudhari, Sneha
AU - Azaria, Amos
AU - Mitchell, Tom
N1 - Publisher Copyright:
Copyright held by the authors.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84991096610&partnerID=8YFLogxK
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AN - SCOPUS:84991096610
SN - 1613-0073
VL - 1688
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 10th ACM Conference on Recommender Systems, RecSys 2016
Y2 - 15 September 2016 through 19 September 2016
ER -