MapRat: Meaningful Explanation, Interactive Exploration and Geo-Visualization of Collaborative Ratings

Saravanan Thirumuruganathan 1 Mahashweta Das 1 Shrikant Desai 1 Sihem Amer-Yahia 2 Gautam Das 1, 3 Cong Yu 4
1 University of Texas at Arlington [Arlington]
2 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
3 Qatar Computing Research Institute
4 Google Inc.
Abstract : Collaborative rating sites such as IMDB and Yelp have become rich resources that users consult to form judgments about and choose from among competing items. Most of these sites either provide a plethora of information for users to interpret all by themselves or a simple overall aggregate information. Such aggregates (e.g., average rating over all users who have rated an item, aggregates along pre-defined dimensions, etc.) can not help a user quickly decide the desirability of an item. In this paper, we build a system MapRat that allows a user to explore multiple carefully chosen aggregate analytic details over a set of user demographics that meaningfully explain the ratings associated with item(s) of interest. MapRat allows a user to systematically explore, visualize and understand user rating patterns of input item(s) so as to make an informed decision quickly. In the demo, participants are invited to explore collaborative movie ratings for popular movies.
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Saravanan Thirumuruganathan, Mahashweta Das, Shrikant Desai, Sihem Amer-Yahia, Gautam Das, et al.. MapRat: Meaningful Explanation, Interactive Exploration and Geo-Visualization of Collaborative Ratings. Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2012, 5 (12), pp.1986-1989. ⟨hal-00922884⟩

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