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Proceedings Article

Visualization and exploration for recommender systems in enterprise organization

[+] Author Affiliations
Z. Karni

Hewlett-Packard Labs. Israel Ltd. (Israel)

L. Shapira

Microsoft Corp. (United States)

Proc. SPIE 8664, Imaging and Printing in a Web 2.0 World IV, 86640E (March 21, 2013); doi:10.1117/12.2013859
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From Conference Volume 8664

  • Imaging and Printing in a Web 2.0 World IV
  • Qian Lin; Jan P. Allebach; Zhigang Fan; Jerry Liu
  • Burlingame, California, USA | February 03, 2013

abstract

Recommender systems seek to predict the interest a user would find in an item, person or social element they had not yet considered, based upon the properties of the item, the user's past experience and similar users. However, recommended items are often presented to the user with no context and no ability to influence the results. We present a novel visualization technique for recommender systems in which, a user can see the items recommended for him, and understand why they were recommended. Focusing on a user, we render a planar visualization listing a set of recommended items. The items are organized such that similar items reside nearby on the screen, centered around realtime generated categories. We use a combination of iconography, text and tag clouds, with maximal use of screen real estate, and keep items from overlapping to produce our results. We apply our visualization to expert relevance maps in the enterprise and a book recommendation system for consumers. The latter is based on Shelfari, a social network for reading and books. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Z. Karni and L. Shapira
" Visualization and exploration for recommender systems in enterprise organization ", Proc. SPIE 8664, Imaging and Printing in a Web 2.0 World IV, 86640E (March 21, 2013); doi:10.1117/12.2013859; http://dx.doi.org/10.1117/12.2013859


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