A common problem in large Urban cities, of the sort seenin Asia, is the huge number of retail options available inthe city. In particular, it is not uncommon to findmultiple malls, each with hundreds of stores inside, just ashort distance from each other in almost every part ofthese cities. These factors make it incredibly hard forconsumers to identify stores of interest to them in anyparticular mall.In response, a number of shopping assistanceapplications have been created for mobile phones.However, these applications mostly just allow users toknow which stores are where or to find promotions onspecific items. What is missing is a system that factors ina user’s shopping preferences and automatically tellsthem which stores are most likely to capture their interest.The key challenge in this system is twofold; 1) building amatching algorithm that can combine user preferenceswith fairly unstructured deals and store information togenerate a final rank ordered list, and 2) designing amobile user interface that can display a large amount ofdeal and shopping information to the user in an easyintuitive way.
Databases and Information Systems | Sales and Merchandising
Software and Cyber-Physical Systems
Asia-Pacific Workshop on Systems APSYS 2012, July 23-24, Seoul
Taylor & Francis
MURALIDHARAN, Kartik; GOTTIPATI Swapna; Jing JIANG; Ramasubbu, Narayanasamy; and BALAN, Rajesh Krishna.
myDeal: The Context-Aware Urban Shopping Assistant. (2012). Asia-Pacific Workshop on Systems APSYS 2012, July 23-24, Seoul. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3241
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