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  1. Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '14)
  2. Shopaholic: a crowd-sourced spatio-temporal product-deals evaluation system (demo paper)
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Evaluating stream predicates over dynamic fields
On the locality of keywords in Twitter streams
Temporal coverage aggregates over moving region streams
Crowd-sourced prediction of pedestrian congestion for bike navigation systems
Road network compression techniques in spatiotemporal embedded systems: a survey
Predicting next location using a variable order Markov model
Modelling movement patterns using topological relations between a directed line and a region
Enumeration of complete set of flock patterns in trajectories
The CMR model of moving regions
Processing real-time sensor data streams for 3D web visualization
Shopaholic: a crowd-sourced spatio-temporal product-deals evaluation system (demo paper)
The vanishing firefly project: engaging citizen scientists with a mobile technology and real-time reporting framework

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Shopaholic: a crowd-sourced spatio-temporal product-deals evaluation system (demo paper)

Content Provider ACM Digital Library
Author Rathinavel, Kruthika Matarazzo, Michael Lu, Chang-Tien Dixit, Gaurav
Abstract The emergence of internet advertising, email marketing and social networking has given rise to a new world of digital advertising used by stores and consumers alike. While retailers aim to promote all types of products, consumers also want to share this information via social media. This paper presents Shopaholic, a system that leverages social media to provide information on trending deals and store sales in any given location. It is intended to help shoppers identify great deals from the vast amounts of data scattered among social networks. Personalized search results, visualization of trends and sentiment analysis provided by Shopaholic allow the user to identify optimal deals. The application accounts for spatial and temporal data via a customized ranking algorithm and features integration with Twitter so that the user can share his or her actual experience using a deal. Ultimately, the system gives back to the shopping community by allowing users to share their experiences and evaluations of deals. A recommendation algorithm uniquely identifies the user's tastes, shopping history and current location to provide deal suggestions, thereby integrating temporal and spatial entities in recommendations.
Starting Page 81
Ending Page 84
Page Count 4
File Format PDF
ISBN 9781450331395
DOI 10.1145/2676552.2676558
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Consumer sentiment Crowd-sourcing Product-deals Social media
Content Type Text
Resource Type Article
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