Share Email Print

Proceedings Paper

Visualization and exploration for recommender systems in enterprise organization
Author(s): Z. Karni; L. Shapira
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 21 March 2013
PDF: 4 pages
Proc. SPIE 8664, Imaging and Printing in a Web 2.0 World IV, 86640E (21 March 2013); doi: 10.1117/12.2013859
Show Author Affiliations
Z. Karni, Hewlett-Packard Labs. Israel Ltd. (Israel)
L. Shapira, Microsoft Corp. (United States)

Published in SPIE Proceedings Vol. 8664:
Imaging and Printing in a Web 2.0 World IV
Qian Lin; Jan P. Allebach; Zhigang Fan, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?