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Research Paper Recommender Systems: A Subspace Clustering Approach

  • Nitin Agarwal
  • Ehtesham Haque
  • Huan Liu
  • Lance Parsons
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3739)

Abstract

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBA) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.

Keywords

Recommender System Hash Table Subspace Cluster Similar User Embed Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nitin Agarwal
    • 1
  • Ehtesham Haque
    • 1
  • Huan Liu
    • 1
  • Lance Parsons
    • 1
  1. 1.Arizona State UniversityTempeUSA

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