NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Making recommendations in Geo-Social Networks
Thomas Mitchell, Colm O’Riordan
CIRG, College of Engineering and Informatics
t.mitchell2@nuigalway.ie, colm.oriordan@nuigalway.ie
Abstract
This paper involves the collection and analysis of
content from the geo-social network Foursquare. This
geo-social network data is then analysed in order to
develop and test new collaborative filtering techniques.
These collaborative filtering techniques are used to
make recommendations (venues, other users) to users of
the foursquare network.
Keywords-Foursquare; Geo-Social Network
Analysis; Collaborative Filtering; Social Media
Introduction
Geo-social networks are a new form of online social
networks (OSN) in which geographic services and
capabilities such as geocoding and geotagging are used
to facilitate additional social interactions.
Foursquare is a location-based social networking
application for mobile devices. Users may "check-in" at
venues and can also find out if any of their friends are
in the same location or if they have checked into any
nearby venues.
The idea behind geo-social networks such as
Foursquare is to encourage real life physical interaction
based on virtual interactions. Currently Foursquare is
the largest pure geo-social network with over 4million
active users. This project involves gathering a dataset,
analysing that dataset and investigating existing and
novel collaborative filtering techniques in order to make
predictions.
Research Approach
The method involved with this research is as follows:
1) Extensively trawling the Foursquare network, more
specifically the most popular venues in New York (a
city with a large number of Foursquare users)
2) Representing this data as a graph which represents
relationships between people and locations and also
between sets of people.
3) Establishing new collaborative filtering techniques to
predict friends and venues that users will like.
4) Analysing the approaches with respect to the quality
of the results obtained using different sources of
evidence and different algorithms for research.
Data Collection
Foursquare presents a public API, which was used to
gather the social network data under investigation. This
data was collected using PHP and OAuth. The most
popular venues in New York were analysed in relation
to users who “liked” these venues. These users then
100
acted as a seed group and from this we ascertained
those users’ friends and the venues they frequently
checked into.
Overview of data collected
The data set can be viewed as a bi-partite graph:
Friends Summary Venues Summary
Vertices/Users 5278
Edges 39548
# Clusters 1029
Directed FALSE
Max degree
mode “all”
1696
Clustering
Coefficient
0.006725015
Graph density 0.004427576
Current & Future Research
We are currently clustering users based on a number of
different factors (venues that they “like”, their friends
“likes” etc…). We will then make predictions using a
number of different machine learning approaches and
present our results.
References
Vertices/Venues 5278
Edges 3
# Clusters 5278
Directed FALSE
Max degree mode
“all”
2
Clustering
Coefficient
N/A
Graph density N/A
[1] S. Scellato, C. Mascolo, M. Musolesi, and V. Latora.
(2010) “Distance matters: geo-social metrics for online social
networks” USENIX Association, Berkeley, CA, USA, 8-8.
[2] Doshi, L., J. Krauss, et al. (2010). "Predicting Movie
Prices Through Dynamic Social Network Analysis." Procedia
- Social and Behavioral Sciences 2(4): 6423-6433