Similar, Yet Diverse: A Recommender System

Collective Intelligence 2014

4 Pages Posted: 9 Apr 2014  

Pinar Ozturk

Stevens Institute of Technology - School of Business

Yue Han

Stevens Institute of Technology - School of Business

Date Written: April 7, 2014

Abstract

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”.

Keywords: Recommender system, latent semantic indexing, collective intelligence, content generation

Suggested Citation

Ozturk, Pinar and Han, Yue, Similar, Yet Diverse: A Recommender System (April 7, 2014). Collective Intelligence 2014. Available at SSRN: https://ssrn.com/abstract=2421608

Pinar Ozturk (Contact Author)

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Yue Han

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

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