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Recommendation in location-based and event-based
social networks
Tomáš Jakab, Michal Vaško, Tomáš Horváth
Ústav informatiky, Prírodovedecká fakulta, Univerzita P.J. Šafárika v Košiciach
Jesenná 5, 040 01 Košice
{tomas.jakab, michal.vasko, tomas.horvath}@upjs.sk
Abstract. With the rapid development of mobile devices and global positioning
system (GPS), location-based social networks (LBSN) have attracted millions
of users to share information about their experiences and tips. Point of Interest
(POI) recommendation system play an important role in LBNS since it can help
users to explore new attractive locations. But there is another phenomenon of
last years, namely event-based social networks (EBSN) with his own event
recommender system, which help users to find appropriate events that best
match their preferences. POI and event recommendation systems definitely
differ from traditional recommendation approaches and have to work at their
own specific circumstances. While POI is basically static point without time
limit, event start and end at specific time and date, thus his lifetime is limited.
In this paper we provide a short survey to both POI and event recommender
systems and point out common and unique problems of these systems. We also
introduce our application whose goal is display list of POIs and events based on
user preferences.
Keywords. Location-based social networks, Event-based social networks,
recommendation
1
Introduction
Location-based social networks (LBSN) and event-based social networks (EBSN)
have become very popular during last years, partially, thanks to new technologies
such as mobile phones. Both types of networks have their own purposes and target
customers. But try to imagine a modelling situation: user visits an unknown city and
has limited free-time. He would like to spend it by sightseeing, i.e. visiting places that
would interest him. For this purpose we tried to unite LBSN and EBSN to create a
unique recommender system, that recommend him as good possibilities to spend his
free-time as possible, offering him a lot of choices for POIs and events that are
actually available at given time and location. In this work in progress article we
present a survey of EBSNs, LBSNs and propose our application.
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Location-based social networks
In LBSNs users share their location via check-in at different types of points-ofinterest (POI) with their network of friends. One of the most popular and well-known
LBSN is Foursquare with more than 55 million people worldwide who left more than
70 million tips and checked-in more than 7 billion times, but there are some others
like Gowalla, Facebook Place, GeoLife, etc. POI recommendation in LBSN inducted
some new challenges. From commerce view of point it is an location-aware
advertisement, from academic point of view it could be mining user preferences from
rich data (social relationships, check-in history, reviews, users tips. etc.) and
recommending new places where users may be interested in. POI recommender
systems differ from traditional recommender systems having the following unique
characteristics.
 Geographical influence. As Tobler’s First law of Geography say
“Everything is related to everything else, but near things are more related than distant
things”. For LBSN it means that users tend to visit nearby POIs rather than distant
ones. Thus, once the user visits one POI, will be more interested in nearby POIs then
distant POIs, even if distant POIs could be more suitable for his preferences.
Geographical influence is the most important feature that discerns POI
recommendation from traditional recommender systems.
 Ratings and sparsity. In traditional recommender systems (Amazon,
Netflix, etc.) users write review for items and leave rate in form of numerical values
falling in determined numerical range and one user usually rate one item just once.
Thus user-item rating matrix is created. On other hand, in LBSN, a frequency matrix
of user check-ins at different POIs is generated, but the range of frequency data can’t
be predetermined, user may check-in for some POIs thousand times while for other
POIs just few times. Another problem is sparsity of user check-ins in frequency
matrix which is usually higher then user-item rating matrix.
 Social influence. Based on assumption that people tend to make social
connections with people with common interest, similar age, gender, etc. means, that
people with social connection tend to like similar POIs. Several studies showed that
knowledge about social relations improve recommender systems. However, studies in
POI recommendation showed that around 96% of users share less than 10% common
visited interests. Hence, social connections has limited influence in POI
recommendation.
 Temporal influence. In traditional recommender systems ratings lose their
importance by time, so temporal influence is used as a factor that decays the weight of
rating. This scenario is also usable in POI recommendation, but there is also another
point of view. Users tent to visit different types of POI at different time at day,
different days in week and different months or seasons. Thus in contrast with
traditional recommender approaches, POI recommendation can be improved by
examination of POI check-in frequencies in time.
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Event-based social networks
As LBSN, also EBSN like Meetup and Plancast became popular in last years. While
LBSN contains POI location, which are static and don’t change in time, like
restaurants and bars, EBSN contains events that are held just once, or in limited time,
such a conference, concert or any meeting of people with common interests. With
large number of events published all over the world it becomes more and more
important to recommend appropriate events to users and avoid stalling them with
unsatisfactory events. However, traditional recommender systems based on
collaborative filtering works poorly in event recommendation since events are actual
just in a limited time range and they can’t be rated before their occurrence. There is a
lack of collaborative data, which raise the issue known as the new item cold-start
problem. One way to obviate this problem is to use user’s intention to attend (or not
attend) events. Furthermore, as LBSN also in EBSN we can use knowledge about
social relations between users. Also location of events is known, so geographical
influence could be investigated. Following are the characteristic properties that differ
event-based recommender systems from traditional recommender systems.
 RSVP. RSVP stands for French expression “répondez s’il vous plaît“
meaning “please respond” and it is used to gain attendance matrix for given
event, thus obviate the problem of lacking ratings. When an event is created,
users can provide RVSPs to it by answering “yes” or “no”. We assume, that
user answering “yes” will attend given event with higher probability then
user who answered “no”. As was showed in [1], approximately 90% of
events have at most 10 RVSPs, causing very high level of sparsity. Also in
[1] it is showed, that 80% of all positive RVSPs were received in the last
20% of their lifetime, what makes recommendation even harder.
 Geographical influence. As in LBSNs, also in EBSN Tobler’s First law of
Geography apply, thus, according to [1], around 50% of users provide
positive RVSPs to events within 10 km of their homes, while users do not
provide RVSPs to events farther then 100 km of their homes.
 Social influence. Social influence is similar to the described one in LBSN.
 Temporal influence. Unlike in LBSN, events does have a specific time and
date where a given event takes place. Thus we need to deal with these
additional information in the recommendation model.
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Application
Our application’s ultimate goal is to provide complex context-aware recommendation
of events to users. An use case, we want to work around, is to recommend
appropriate events to user in foreign town for specific time range based on user’s
history (reviews and ratings of other events, his attendance on them), specifics of
given place (what type of events can user find in given area) and external influences
(e.g. weather, season, etc.). Since we just started to work on our project, recently, we
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are far away from this goal at this moment. Up to date we are dealing with two main
parts – gathering events from different events-oriented web pages and development of
the application itself.
Application is divided, as usual for web applications, into two parts - server side and
client side. Apparently from the use case, application is aimed for active people who
want to spend free time at interesting events, thus application’s client side is
developed for mobile devices. Currently we are developing a version for mobile
phones and tablets with OS Android which is the most expanded OS in Slovakia (in
2014 Android beat iOS with twice as much devices on market). However, widespread
use and popularity of Android brings also some disadvantages and from
implementation’s viewpoint it is quite a headache for designers to create uniform
design for so colourful palette of devices with different screen sizes and resolutions.
Detailed description of application’s server side is out of the scope of this paper.
Events are classified into 11 categories (food&drink, sport, music&dance, history,
freetime, movie&theater, arts, indoor, outdoor, education, other) and gathered from
two main sources – Facebook and event-oriented websites. These are some common
and also unique problems we are facing during events gathering.
 Obtaining GPS coordinates. If the downloaded event doesn’t include
explicit GPS coordinates but includes address information (town, street
name, house number) we try to obtain coordinates through the Google Places
API or from the geonames.org town’s database. The found address as well as
obtained GPS coordinates are then saved to our database in order to
minimalize the use of external APIs. Thus we are able to search for users
nearby events.
 Date and time extraction. As we mentioned above, knowledge about the
time and date of an event is crucial if we want to recommend events
accordingly to user’s free time. We extract this information from event's
description. We categorize events to whole-day events and events with
specific start and finishing time. Some events, like art exhibitions or film
screenings, could periodically repeat. Some websites consider periodically
repeated events as one event with more dates, and other websites consider a
repeated event as a new event with every new date, thus our system has to
maintain these situation.
 Duplicity of events. We are downloading data from different sources, thus it
is probable to download the same event more times from different sources.
To avoid these situation we developed a simple TF/IDF-based algorithm
looking for and revealing possible event duplicities.
Currently these two sources are used to crawl events:
 Facebook API. The application allows users to use their Facebook account
to login, thus we can browse and download (after user’s permission) events
connected to his/her account. However, events from Facebook usually don’t
contain information about event’s category, so we use NLP analysis to
obtain event’s keywords (persons, places, etc.) from their description.
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Afterwards keywords' frequencies are evaluated and the event is classified
into one of the mentioned categories. Another problem with events from
Facebook is that event’s diversity is conditional to taste of users who use our
application. Facebook is full of popular events like pop music concerts, but it
is much more difficult to find events related to history or classical music.
The statistics of correctly classified events and count of events by categories
are introduced in Table 1. Bad classification accuracies of rare types of
events are since the data are imbalanced, so we will apply some techniques
from the field of classification of imbalanced data in our future work.
Correctly classified
food&drink
sport
music&dance
history
freetime
movie&theater
arts
indoor
outdoor
education
other
515 of 798 ≈ 64.53%
8 of 23 ≈ 34.78%
4 of 12 ≈ 33.33%
448 of 516 ≈ 86.82%
0 of 1 ≈ 0%
1 of 30 ≈ 3.33%
12 of 48 ≈ 25%
1 of 21 ≈ 4.76%
2 of 11 ≈ 18.18%
9 of 22 ≈ 40.91%
28 of 47 ≈ 59.57%
2 of 64 ≈ 3.12%
Table 1 Facebook events

Event based websites. Event based websites include websites about tourism,
websites dedicated to searching concerts, theatre performances etc., but also
government’s websites about cultural events. Up to date, we use only
Slovakian web pages for data gathering. Counts of events gathered from
event based websites are in Table 2.
Category
food&drink
sport
music&dance
history
freetime
movie&theater
arts
indoor
outdoor
other
# of unique
74
73
576
41
9
825
220
129
10
505
2462
total
77
73
752
41
46
2359
9575
912
58
3225
17118
Table 2 Event based websites
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Conclusions
We presented our work-in-progress project in this paper concerned with the
development of an event recommender system for tourists. Since the development is
rather in the beginning, we first focused on modules for data gathering while kept the
recommendation engine for future work. We presented here some issues encountered
during our research and development of the application on which we will have to
focus in out further work. Since there are many unsolved and challenging issues yet
unresolved in the area of event recommendation, we believe that our application will
foster our research and serve as a good starting point for further development.
References
1. Augusto Q. de Macedo, Leandro B. Marinho: Event Recommendation in Event-based
Social Networks. Social Personalization Workshop (2014)
2. Yonghong Yu, Xingguo Chen: A Survey of Point-of-Interest Recommendation in
Location-Based Social Networks. AAAI Workshop (2015)
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