MapRat: meaningful explanation, interactive exploration and geo-visualization of collaborative ratings

Authors:
Saravanan Thirumuruganathan;Mahashweta Das;Shrikant Desai;Sihem Amer-Yahia;Gautam Das;Cong Yu
Affiliations:
University of Texas at Arlington;University of Texas at Arlington;University of Texas at Arlington;Qatar Computing Research Institute;University of Texas at Arlington and Qatar Computing Research Institute;Google Research
Venue:
Proceedings of the VLDB Endowment
Year:
2012

Citing 1
Cited 4

Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals

Data Mining and Knowledge Discovery

The data analytics group at the qatar computing research institute

ACM SIGMOD Record
Exploratory mining of collaborative social content

Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
New perspectives for recommendations in location-based social networks: time, privacy and explainability

Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
Mobility and social networking: a data management perspective

Proceedings of the VLDB Endowment

Quantified Score

Hi-index	0.00

Visualization

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.