Subventions et des contributions :

Titre :
Ranking Experts in Location-aware Question-Answering Services
Numéro de l’entente :
EGP
Valeur d'entente :
25 000,00 $
Date d'entente :
7 mars 2018 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Ontario, Autre, CA
Numéro de référence :
GC-2017-Q4-01400
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Nom légal du bénéficiaire :
Papagelis, Manos (Université York)
Programme :
Subventions d'engagement partenarial pour les universités
But du programme :

With the growing use of mobile devices, users increasingly expect to find more local and time-sensitivex000D
information, such as the new popular restaurant or the hidden café around the corner. In this project, we aim tox000D
leverage big data and machine learning to address the problem of providing accurate and timely answers to userx000D
questions from local experts. In particular, the industrial partner has developed a mobile app that facilitatesx000D
location-based question-answering services. These services allow users to use their mobile phone to askx000D
questions and find local information by interacting with local experts. A key to the success of these services isx000D
the quality and timeliness of the responses provided by directing user questions to the right expert in ax000D
database. The main objective of this collaborative project is to address this problem by designing andx000D
developing algorithms for finding and ranking experts in a database.x000D
The anticipated outcome of the research is twofold: (i) a framework for automatically enhancing a user'sx000D
expertise profile, and (ii) novel algorithms, strategies and tools that can find and rank local experts that canx000D
provide useful and timely answers. The scientific approach of our research is characterized by processing,x000D
modeling, analysis of large data sets and involves the use of advanced techniques for data analysis, such as textx000D
analytics, machine learning, predictive analytics, and natural language processing.x000D
The proposed research collaboration aligns with Canada's Innovation Agenda. Conducting research in thex000D
intersection of big data analytics and machine learning has the potential to attract the brightest students fromx000D
around the world, while keeping domestic talent here. Meanwhile, it will contribute to the training of highx000D
quality personnel, with first-rate analytical and problem-solving skills that have the capacity to stimulatex000D
knowledge-based companies and boost productivity and innovation taking place across Canada.