An entity graph based Recommender System

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...

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Bibliographic Details
Published in:Ai communications 2017-01, Vol.30 (2), p.141-149
Main Authors: Chaudhari, Sneha, Azaria, Amos, Mitchell, Tom
Format: Article
Language:English
Subjects:
Electronic devices
Measurement methods
Multimedia computer applications
Recommender systems
Search engines
Similarity
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Summary: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.
ISSN:0921-7126
1875-8452
DOI:10.3233/AIC-170728