What started as a class project quickly soon became a more serious effort to build ‘Shopaholic’. This project was accepted at the Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming, 2014.
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 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.View/Download Paper