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Accessible POI Recommendation Using Adaptive Aggregation of Binary Ratings

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  • Tue, 04/24/2018 - 10:00am - 12:00pm




Accessible POI Recommendation Using Adaptive Aggregation of Binary Ratings

MSc Thesis Proposal by:

Bidur Subedi

Date:  Tuesday, April 24th, 2018
Time:  10:00 am – 12:00 pm
Location: 122, Essex Hall

Abstract: Everyone needs one or more forms of accessibility at some point in life due to age, medical conditions, accidents, etc. People with accessibility needs have right to accessible services, as well as the right to information about accessible places or Points of Interest (POI). While most popular POI recommendation services do not take accessibility into account, some of them only consider a few specific needs, such as ramp for wheelchair users. However, different users have different accessibility needs regarding the structure of building, special aid devices, and facilities to be able to independently visit a place. The proposed system focuses on finding the personalized accessibility score for a (user, POI) pair. It can be used with other factors such as historical behavior, social influence, geographical conditions, etc. to recommend accessible places. It uses time decaying aggregate on the crowd-sourced binary rating data to find accurate approximation of current accessibility status for each accessibility criteria. Also, we propose a tunnel based algorithm to detect the trend of binary stream data and update the rate of decay. This ensures that the calculated aggregate adapts to change in the accessibility status of the place.

Thesis Committee:
Internal Reader: Dr. Dan Wu
External Reader: Dr. Gokul Bhandari
Advisor: Dr. Stephanos Mavromoustakos
Co-Advisor: Dr. Xiaobu Yuan

 



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