Similar, Yet Diverse: A Recommender System
Stevens Institute of Technology - School of Business
Stevens Institute of Technology - Stevens Institute of Technology - School of Business
April 7, 2014
Collective Intelligence 2014
Social Tagging or Collaborative Tagging applications allow users to create and share lightweight metadata in the form of chosen keywords called tags to represent the created content.These tags help users to self-organize, share and find content they are interested in. Since tags are local descriptions of content provided voluntarily by users, they represent personalized information both about the user and the created content which can later be used for the creation of recommender systems. In this paper, we propose a recommender system for the Scratch online community. The proposed recommender system utilizes project tag information to determine similarities between various users and then uses these relationships to identify the optimal set of items to be recommended. Through a calculated combination of relevancy and diversity, our recommender system is aimed at leading users to explore further into the Scratch community and improving the productivity of “passive producers” by using the output of “active consumers”.
Number of Pages in PDF File: 4
Keywords: Recommender system, latent semantic indexing, collective intelligence, content generation
Date posted: April 9, 2014
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