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 scalable platform for computing content-based recommendations.
Hadoop is employed for content characterizing and profile matching for content based recommendation in REF .
CONTENT-BASED RECOMMENDATION ALGORITHMS ON THE HADOOP MAPREDUCE FRAMEWORK
WEBIST
Computer Science