Authors:
Toon De Pessemier
;
Kris Vanhecke
;
Simon Dooms
and
Luc Martens
Affiliation:
Ghent University, Belgium
Keyword(s):
Recommender systems, Cloud computing, Hadoop, MapReduce, Content-based recommendations.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Ontologies and the Semantic Web
;
Personalized Web Sites and Services
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Personalization
Abstract:
Content-based recommender systems are widely used to generate personal suggestions for content items based on their metadata description. However, due to the required (text) processing of these metadata, the computational complexity of the recommendation algorithms is high, which hampers their application in large-scale. This computational load reinforces the necessity of a reliable, scalable and distributed processing platform for calculating recommendations. Hadoop is such a platform that supports data-intensive distributed applications based on map and reduce tasks. Therefore, we investigated how Hadoop can be utilized as a cloud computing platform to solve the scalability problem of content-based recommendation algorithms. The various MapReduce operations, necessary for keyword extraction and generating content-based suggestions for the end-user, are elucidated in this paper. Experimental results on Wikipedia articles prove the appropriateness of Hadoop as an efficient and scalab
le platform for computing content-based recommendations.
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