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EP/K019392/1 - GALE: Global Accessibility to Local Experience

Research Perspectives grant details from EPSRC portfolio

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Dr C Mascolo EP/K019392/1 - GALE: Global Accessibility to Local Experience

Principal Investigator - Computer Laboratory, University of Cambridge

Scheme

Standard Research

Research Areas

Human-Computer Interaction Human-Computer Interaction

Information Systems Information Systems

Related Grants

EP/K020552/1

EP/K020633/1

Start Date

04/2013

End Date

09/2015

Value

£283,957

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Grant Description

Summary and Description of the grant

Recommender Systems have been generated in the past 15 years with the aim to suggest to individual users opportunities arising in the virtual space of the Internet on the basis of the individual profile of the user, her/his past history as a customer/web-user and even her/his friends' community in social networks. Further, the rise of online social networks such as Facebook has allowed for a new source of information to be exploited by recommendation systems: the user social network.
Internet access is now becoming increasingly mobile and smart phones are changing the way people interact with places and with each other in an increasingly complex manner. Smart phones are starting to impact the way users access information on the go and receive suggestions. More specifically, innovative recommender systems are currently being developed to exploit GPS-based or other location-sensitive information, associated on-the-go to individual users through smartphones. This second generation of recommender systems, by being location-based, pose an entirely different set of problems which not only have to do with the knowledge of the user (her or his "profile"), but also with that of the places. Knowledge of places can be achieved by means of guides, textbooks and journey reports, or by direct experience. These ways are quite different in nature. The former is globally accessible (everybody can get it from afar) and relatively fast to obtain, especially in the age of the Internet. The latter is only locally accessible (one needs to be in the place to access it) and, being generated by those living in the place through personal local interaction, it becomes accessible only after long-term interactions and the construction of personal relationship of mutual trust. When visiting a new place, you would necessarily rely only on global information to navigate the place and access its resources. Conversely, if you are a local, your knowledge of the place is mostly constructed through your personal long-term exchange with what all your neighbors are doing every day and with their favorite places in the neighborhood; as a result, you not only would rely on local knowledge, but you would also contribute - by interacting locally - to the formation and continuous re-shaping of the information used by your neighbors too in their interaction. If we name the long-term, locally generated knowledge of the place "neighborhood knowledge", we can say that what people locally do in places is in one way or another dependent on the extent to which they have access to the neighborhood knowledge.
The second generation of recommender systems allows "global" place-users, i.e. people visiting a place who are not experienced with the place itself, to access "globally" available information. However, a good deal of information is still not exploited in these systems, as the geographic and the social only "meet" in a superficial way: in other words, the system does not take advantage of any information about the particular use of the place that local "communities" have done in the past and do "at the moment".
As neighborhood knowledge information is now becoming increasingly available through the viral expansion of location-based social networks such as foursquare or Gowalla, it is now possible to explore a third generation of recommender systems, where knowledge about how the place had been used in the past (historical use) or is used at the moment of the inquiry (real-time use) by communities of users is the key element of the system. The main motivation behind the GALE project is to pioneer such third generation recommender systems which would make it possible for the rapidly growing population of "global" city users to access a level of information, that of the neighborhoods knowledge, which is inherently inaccessible to global repositories, and to do that in real time.

Structured Data / Microdata


Grant Event Details:
Name: GALE: Global Accessibility to Local Experience - EP/K019392/1
Start Date: 2013-04-01T00:00:00+00:00
End Date: 2015-09-30T00:00:00+00:00

Organization: University of Cambridge

Description: Recommender Systems have been generated in the past 15 years with the aim to suggest to individual users opportunities arising in the virtual space of the Internet on the basis of the individual profile of the user, her/his past history as a customer/web-u ...