Extending Content-Boosted Collaborative Filtering for Context-aware,
Mobile Event Recommendations
Daniel Herzog and Wolfgang W
Department of Informatics, Technische Universit
at M
unchen, Boltzmannstr. 3, 85748 Garching, Germany
Recommender System, Event Recommendations, Content-Boosted Collaborative Filtering, Context-
awareness, Mobile Application.
Recommender systems support users in filtering large amounts of data to find interesting items like restaurants,
movies or events. Recommending events poses a bigger challenge than recommending items of many other
domains. Events are often unique and have an expiration date. Ratings are usually not available before the
event date and not relevant after the event has taken place. Content-boosted Collaborative Filtering (CBCF)
is a hybrid recommendation technique which promises better recommendations than a pure content-based or
collaborative filtering approach. In this paper, CBCF is adapted to event recommendations and extended by
context-aware recommendations. For evaluation purposes, this algorithm is implemented in a real working
Android application we developed. The results of a two-week field study show that the algorithm delivers
promising results. The recommendations are sufficiently diversified and users are happy about the fact that
the system is context-aware. However, the study exposed that further event attributes should be considered as
context factors in order to increase the quality of the recommendations.
Recommender systems are information filtering and
decision support tools providing personalized recom-
mendations by identifying information or products
which best satisfy the user’s needs. Recommender
systems are increasingly used in a mobile context
due to the widespread use of smartphones and tablets.
These devices allow more accurate recommendations
because they can identify the context of the recom-
mendation in a more detailed manner (Ricci, 2011).
In general, context can be described as ”any informa-
tion that can be used to characterize the situation of
entities (i.e., whether a person, place, or object) that
are considered relevant to the interaction between a
user and an application, including the user and the
application themselves” (Dey et al., 2001). Mobile
devices are able to collect this type of information
as they are often equipped with sensors which allow
them to identify context data like the user’s current
position or speed of travel.
Event recommendations pose a new challenge in
the field of recommender systems. Minkov et al.
(2010) explain that events, as opposed to movies or
restaurants, usually take place only one-time under
the same conditions, thus they come with an expi-
ration date. User ratings, which can be considered
for recommendations, are usually not available be-
fore the event takes place and no longer of importance
when the event is over or expired. This lack of ratings
makes it impossible to recommend, for example, fu-
ture events other users with similar preferences have
liked. Hence, additional data has to be collected and
processed by more sophisticated techniques in order
to generate recommendations for events. Event rec-
ommendations are moreover a good example show-
ing the importance of context in recommender sys-
tems. For instance, in case of a bad weather forecast,
recommendations for outdoor events could be inap-
Different techniques are available for recommend-
ing items such as events. Hybrid recommenders use
the combination of two or more techniques in order
to overcome weaknesses of single techniques and to
improve the quality of recommendation. Melville
et al. (2002) present Content-boosted Collaborative
Filtering (CBCF), a hybrid recommendation tech-
nique which generates better recommendations than
a pure content-based (CB) or a pure collaborative fil-
tering (CF) approach by combining these two tech-
The main goal of this work is to identify new
methods to improve the quality of event recommenda-
Herzog, D. and Wörndl, W.
Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations.
In Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 2, pages 293-303
ISBN: 978-989-758-186-1
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
tions in a mobile scenario. For this purpose, we intro-
duce a new approach to generate personalized event
recommendations. To the best of our knowledge, we
are the first to use CBCF within a context-aware rec-
ommender to recommend all kinds of cultural events.
As a first step, this paper presents important recom-
mendation techniques and CBCF is explained. We
further explain the details of our algorithm and show
how it can be used for recommending events. Our
Android application which implements the context-
aware CBCF algorithm is then presented and used for
the field study of the system. After discussing our
findings, we present related work in the field of rec-
ommender systems and event recommendations. The
paper finishes with a conclusion and an outlook on
future work in this area.
Different techniques and algorithms can be imple-
mented in recommender systems in order to gener-
ate personalized recommendations. These techniques
differ in the information they collect and how they
rate items. Two popular techniques are CB predic-
tions and CF.
CB systems try to recommend items which are
similar to those the user has liked in the past (Bala-
c and Shoham, 1997). A profound knowledge
of the item representation is mandatory because CB
recommender systems analyze item descriptions and
attributes to identify similar items. Pazzani and Bill-
sus (2007) differentiate between structured and un-
structured data to represent items: items represented
by structured data comprise a set of attributes and
there is a known set of values that each of these at-
tributes may have. Unstructured data have no well-
defined values, e.g., a text field allowing entry into ev-
ery possible text. In most cases, semi-structured data,
a combination of attributes with a known set of val-
ues and free-text fields, is chosen. One advantage of
CB recommending is that no critical mass of users is
necessary to provide recommendations. Nevertheless,
CB recommending comes with some limitations. As
the New User Problem explains, a new user has to rate
a significant number of items before the system can
offer accurate recommendations for her or him. Fur-
thermore, CB systems aim to recommend items which
tightly fit a user’s profile, thus a lack of diversity can
be an issue (Adomavicius and Tuzhilin, 2005).
Case-based recommendations are a special case
of CB recommenders. According to Smyth (2007),
cases are solutions to a given recommendation prob-
lem. The simplest scenario is the recommendation of
the top k most similar cases matching a user query.
Case-based recommendations can be used for recom-
mending items with a structured item representation
as they allow the use of sophisticated approaches to
judge how cases respond to the user query. Formula 1
is one exemplary approach how to calculate the simi-
larity between a case and the query:
Similarity(q, c) =
· sim
, c
, (1)
(Smyth, 2007), where n is the number of attributes
of the item. sim
is the similarity between an attribute
i of a query q and a case c. The attribute similarity
can be calculated with formula 2 and is weighted by
, c
) = 1
, c
(Smyth, 2007). The presented formulas depend on
the selected similarity metric. Smyth differentiates
between symmetric and asymmetric similarity met-
rics. A symmetric similarity metric reduces the sim-
ilarity by the same value if the case attribute value
is lower or higher than the query attribute value. An
asymmetric metric prefers either higher or lower val-
In contrast, CF recommends items other users
with similar preferences have liked (Balabanovi
c and
Shoham, 1997). In the majority of cases, nearest
neighbor algorithms are implemented in CF recom-
menders. Schafer et al. (2007) differentiate between
User-Based Nearest Neighbor and Item-Based Near-
est Neighbor algorithms. While User-Based Nearest
Neighbor algorithms call users who rate objects sim-
ilar neighbors, Item-Based Nearest Neighbor algo-
rithms rate items based on similarities between items
(Sarwar et al., 2001). Like CB systems, CF has some
limitations. In addition to the New User Problem,
the New Item Problem is an issue because items not
rated by a substantial number of users cannot be rec-
ommended (Adomavicius and Tuzhilin, 2005). These
problems define the Cold-Start Problem - a serious
problem for event recommendations. Because users
cannot rate unique future events before actually at-
tending them, most of the items in the system remain
unrated (Dooms et al., 2011). The limitation of such a
sparely filled user-item rating matrix is called sparsity
(Melville et al., 2002). Sparsity causes a low proba-
bility of finding a set of users with significantly simi-
lar ratings, thus leading to fewer or no recommenda-
The presented limitations of CB and CF recom-
menders can be illustrated using a simple example.
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
Table 1 shows a user-item rating matrix with two
users A and B and five events. Four events are as-
signed to the genre comedy, thus they are assumed to
be similar. The other event is a musical. User A al-
ready provided a good rating to two comedy events,
indicated by the + symbol. User B gave positive rat-
ings to the two other comedy events and to the musi-
cal. A CB recommender system is able to recommend
to each user the comedy events not previously rated
as these events are similar. As CB recommendation is
not taking other users into account, no further recom-
mendations for User A are possible. A CF approach,
however, is not able to recommend any event to any
user. User A and B had not initially rated any com-
mon events, thus they cannot be identified as neigh-
Table 1: Exemplary user-item rating matrix for events.
User A User B
Comedy 1 +
Comedy 2 +
Comedy 3 +
Comedy 4 +
Musical 1 +
Hybrid recommender systems combine two or
more techniques in order to overcome such limi-
tations. One example of a hybrid recommending
method is CBCF. CBCF is a feature augmentation
hybrid which uses the output from a CB prediction
to generate recommendations in the subsequent CF
phase (Burke, 2002). The initial CB prediction is ex-
ecuted on the user-item rating matrix containing all
ratings given by the users. The predicted ratings are
then stored in the user-item rating matrix, now called
pseudo user-item rating matrix, which is character-
ized by a lower sparsity. Finally, the CF algorithm is
executed on the pseudo matrix (Melville et al., 2002).
In the presented example (Table 1), the CB approach
extends the user-item rating matrix by CB predictions,
in this case, two comedy events for each user. Based
on the new pseudo matrix, CF is able to identify User
A and B as neighbors. This allows the recommenda-
tion of the musical to User A since it was highly rated
by a user with similar preferences.
The example presented in Table 1 illustrates the
strengths of hybrid recommenders. CB recom-
menders identify events similar to those the user al-
ready liked. To arrive at this conclusion, past feed-
back has to be analyzed but no critical mass of users
is necessary. CF promises an increase in the variety
of the recommendations but because of the lack of
ratings for future events, CF has to be combined with
other techniques. This is why we use CBCF for our
event recommender as it combines these two tech-
niques and promises better recommendations than
pure CB or CF recommenders (Melville et al., 2002).
Furthermore, we want to show that CBCF can be used
for context-aware recommendations. We briefly intro-
duced the idea of our algorithm and first results of a
field study in (Herzog and Woerndl, 2015).
Adomavicius and Tuzhilin (2011) present three
different paradigms for incorporating contextual in-
formation into a recommendation process. Our sug-
gested context-aware CBCF approach implements
contextual pre-filtering to diminish the amount of
events available for recommendations before the ac-
tual recommendation process takes place. This ap-
proach is advantageous because events which are im-
possible to recommend are excluded immediately: for
example, an event taking place too far away from the
user’s location. In future work, we plan to try the
other paradigms for incorporating context as well and
compare those results to the solution presented in this
After the pre-filtering phase, our algorithm ana-
lyzes user feedback on past recommendations in or-
der to predict the ratings of the remaining events in
the CB recommendation phase. These predictions are
entered in the pseudo rating matrix. Finally, the CF
phase is executed in order to consider all predicted
ratings of all users and identify additional recommen-
dations. This step is necessary to increase the vari-
ety of our event recommendations. To sum up, the
proposed recommendation algorithm comprises three
1. Contextual pre-filtering
2. Content-based prediction
3. Collaborative filtering
In the following, we describe these phases in detail.
Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations
3.1 Contextual Pre-filtering
Before predicting ratings for the events available in
the database, the number of possible recommenda-
tions is decreased by taking context into account. Rel-
evant context factors in this work were identified in
expert interviews with selected representatives from
the German event and ticketing industry. These con-
text factors are:
Current Position: It is likely that the user prefers
selected venues but the travel distance to these
venues has to be appropriate. The system should
be able to identify the user’s current location and
the user should be able to set a radius around it.
Only venues within this radius should be consid-
ered for recommendation.
Temporary Preference of Selected Genres: The al-
gorithm should ignore certain genres during the
recommendation process, e.g., when a genre is in-
appropriate for the user’s companions. The user
should be able to select or deselect genres in or-
der to tell the system the appropriate genres.
Budget: The algorithm has to respect the user’s
available budget. For this purpose, the user has to
set an upper limit for event prices or the sum she
or he can spend per week or month.
Weekday: Recommendations have to respect the
identified days the user is available for events
(e.g., only on weekends). The user should be
able to tell the system the weekdays for the pre-
filtering of events.
Time of Day: Recommendations are only useful
when the user is available at the suggested time
of day (e.g., not during the morning). The user
should be able to tell the system the times of day
for the pre-filtering of events.
Scheduling Conflict: If the user already purchased
tickets for a recommended event, no further events
which take place at the same time should be rec-
ommended. The recommender system has to
identify such conflicts automatically and exclude
events if necessary.
As described, these factors are used as criteria for
exclusion. If the context of a potential recommenda-
tion exceeds a defined threshold, e.g., the distance to
the venue, the corresponding event will not be consid-
ered for recommendation. The developed prototype
in section 4 allows the user to set and modify these
thresholds as explained above.
3.2 Content-Based Prediction
After excluding events which do not satisfy the con-
text constraints, the classical CBCF approach can be
executed. At first, the CB prediction phase of CBCF
has to be adapted to the special case of event recom-
mendations. In this section, we describe how we an-
alyze event attributes rated by the user in the past in
order to estimate ratings for events comprising these
attributes. At this point, context is no longer the fo-
cus. Nevertheless, the presented context factors are
reflected in the item representation during the CB pre-
diction, e.g., when determining how much the user
likes a certain venue.
As explained, items can be represented by struc-
tured data, unstructured data and semi-structured
data. Based on the expert interviews we conducted,
events are mainly characterized by structured data. A
Munich-based event and ticketing company provided
a dataset with approximately 3700 real events which
were used for the survey in section 5. The dataset in-
cludes the following, relevant event attributes:
The event name
The name and address of the venue
The genre
The exact date when the event starts
The vendor
The dataset did not provide information about the
ticket price which therefore was not considered in this
work. The structured characteristic of events is the
reason why we propose a case-based approach for the
CB prediction of ratings.
In order to adapt CBCF to event recommenda-
tions, we propose a slightly different similarity metric
than the symmetric and asymmetric similarity metrics
presented in section 2. Nominal case attributes such
as the genre or the venue are treated as binary values.
If an event takes place at a certain venue, its value is
1 for this venue and 0 for all other venues. The query
attributes depend on the user’s history. If a user liked
90% of all recommendations of a venue, the query
value q
for this venue is 0.9. The attribute similar-
ity is calculated with Formula 2 using q
equals 0.9
and the case value c
equals 0 or 1 as input. Other
attributes, such as the event’s price, could apply an
asymmetric similarity metric. If the price is lower
than the average as identified by the user’s history, the
similarity will be less reduced than for a higher price.
The challenge is to find a way to calculate the
query value q
for each attribute. As described, user
feedback for an upcoming event is usually not avail-
able up-front, hence the user’s history has to be used
as a basis for the calculation. For event recommenda-
tions, two scenarios are possible: the user either likes
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
or rejects a recommendation. Additionally, if the user
actually purchases tickets for a recommended event,
this would be considered a more positive feedback
than just liking the recommendation. In this work, we
count all liked, rejected and purchased recommenda-
tions and calculate the share of the positive feedback.
Thereby, liked events are increased by factor 3 and
events with purchased tickets by factor 5 as we as-
sume that users often have to reject recommendations
only because of time constraint issues. If a user liked
one theater recommendation, rejected another theater
recommendation but purchased tickets for a third the-
ater performance, q
for i = theater will be calculated
0.8889. This value means that the user likes
the attribute theater in a recommendation with a prob-
ability of 88.89%.
If q
can be calculated for a sufficient number of
attributes, i.e., past feedback is available for these at-
tributes, a prediction is called accurate. Formula 1 can
be used to calculate the similarity between the item
and the query corresponding to the value of the rec-
ommended item for the user. This value is stored in
the pseudo user-item rating matrix and used for the
upcoming CF phase.
3.3 Collaborative Filtering
Recommendations which were not rated during the
CB phase are candidates for the upcoming CF phase.
Rating recommendations in the CF phase is of prime
importance because focusing solely on the user his-
tory could lead to a poor diversity of recommen-
dations. As explained, User-Based Nearest Neigh-
bor and Item-Based Nearest Neighbor algorithms are
available for CF. In this work, we implement a User-
Based Nearest Neighbor algorithm as it is already
used for the CBCF approach in (Melville et al., 2002).
Some assumptions have to be made to reach valu-
able predictions. Only users with a similarity of at
least 50% are considered as neighbors. The CF fills
the pseudo matrix with additional ratings. In the end,
the events with the highest value for the user are rec-
ommended. Every recommendation has to have a
value of at least 0.5 on a scale from 0 to 1. In or-
der to achieve a high serendipity, we divide the events
in three groups. Around one third of the recommen-
dations are events which take place within 7 days,
one third within one month and one third not within
one month. If not enough events are available for a
group, the list of recommendations is filled with the
best available recommendations. We limit the max-
imum number of recommendations at one request to
10 to avoid overwhelming the user with events. As
a result, our context-aware CBCF method is able to
recommend a set of events which respect the user’s
context and expectations and promise a satisfying di-
We developed an Android application to implement
our algorithm and to evaluate the recommender sys-
tem. The application can be installed on devices run-
ning Android 4 or newer.
For our prototype, the dataset is stored in a
MySQL database connected to a RESTful web ser-
vice we developed. When a client demands recom-
mendations from the web service, it transmits the user
id and the current context information to the server.
The web service then is able to provide personalized
recommendations based on the user-item rating ma-
trix which is stored centrally in the MySQL database.
These recommendations are received by the applica-
tion and displayed to the user.
In this section, we briefly present the applica-
tion. The application can be used without a login. A
unique, device-dependent id allows personalized rec-
ommendations without creating a user account. After
starting the app, a request is sent to the server auto-
matically and the user immediately receives a list of
new recommendations (Figure 1). Recommendations
are presented as cards, a design principal made pop-
ular through applications such as Tinder and Google
Now. The main advantages of this representation are
the low cognitive load because of the small number
of items visible and the fact that the user can navigate
through the app and provide feedback with only one
hand (Torkington, 2014). In the developed app, the
cards provide the most interesting information about
the recommended event such as its name, the genre,
the location and the date. Furthermore, the calculated
rating for the user is expressed as a percentage and
presented together with a short explanation. For ex-
ample, the explanation ”dein Feedback” (German for
”your feedback”) indicates that the event is recom-
mended because of a rating from the CB recommen-
dation phase. The user can swipe recommendations to
the right to give a positive rating to a recommendation
and to the left in order to reject the recommendation
and to provide a negative rating.
Clicking on a recommendation displays a detailed
view of the selected event. At the selected event, the
user can find additional information concerning the
venue or find Facebook friends who are attending the
event. A button linking to the ticket vendor is also
available. For testing purposes, the prototype assumes
Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations
Figure 1: New recommendations provided by the recom-
mender system.
that a user clicking this button eventually purchases a
Using the Navigation Drawer, the app’s menu bar,
the app user can call up the settings view allowing her
or him to modify the thresholds for the contextual pre-
filtering (cf. section 3.1). Figure 2 shows a screen-
shot of the geographical context settings. The user
can choose the radius threshold for the contextual pre-
filtering of events by moving the slider thumb. The
radius is drawn around a certain position which can
be either determined by activating the device’s GPS
sensor or chosen from a predefined list with cities. In
this example, the current user position is set to the
city center of Munich and only events within a ra-
dius of 25 kilometers are considered for recommen-
dation. In the second settings view, the user can de-
termine her or his desired weekdays and times of day
for the pre-filtering phase by selecting the correspond-
ing time slots (Figure 3). For this purpose, we offer
a calendar view which splits every day of the week
into five times of day from morning (6 am until 11
am) until night (10 pm until 6 am). This design al-
lows the user to select time slots for each day individ-
ually. This selection option is important for event rec-
ommendations because we believe a potential visitor
cannot attend events every day at the same time. The
additional check box allows users to select and dese-
lect all slots at once. The third setting view displays a
list of genres which can be selected or deselected by
the user in order to determine appropriate genres for
the pre-filtering phase.
Figure 2: Geographical context settings.
Figure 3: Temporal context settings.
In this section, we describe a field study conducted to
evaluate the developed recommender system and the
context-aware CBCF algorithm. The study was con-
ducted after the implementation of the presented sys-
tem. The main goal of the study was to test the rec-
ommender system in a realistic scenario. The study
was intended to deliver insights into the quality of the
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
recommender system. The method of influencing the
context-awareness in the app settings also had to be
evaluated. The study results are presented and inter-
preted at the end of this section.
5.1 Setting and Procedure
The field study was conducted as a two-week beta
test, meaning, the users installed the application on
their own smartphones. Users were advised to use the
application as if they were using an application they
installed voluntarily in everyday life. This means that
during the test, users were allowed to use the system
whenever and wherever they wanted to, in the desired
intensity. Nevertheless, they were recommended to
use the app at least for a certain amount of time to
get a first impression of the system and the delivered
After two weeks, the beta test terminated and a
survey was sent to the participants. The main objec-
tive of the survey was to evaluate: the algorithm, the
offered method to influence the context-awareness,
the user interfaces and the recommender system as a
whole. Table 2 lists all evaluation statements included
in the survey. The participants had to rate these state-
ments using a 5-point Likert scale with 1 represent-
ing no agreement at all and 5 representing complete
Table 2: Survey statements.
# Statement
1 Overall, I like the recommender system
2 The recommendations meet my expecta-
3 The recommendations are sufficiently di-
4 My feedback leads to better recommenda-
5 The system provides sufficient means to
express my expectations
6 I like the way I can set local constrains
7 I like the way I can set temporal constrains
8 I like the way I can choose certain genres
9 The user interfaces are intuitive
10 I like the presentation of new recommen-
Further personal questions were added to the sur-
vey in order to obtain an overview of the participant’s
background, personal experience with events and per-
sonal experience with similar recommender systems.
5.2 Participants
The participants were selected to achieve a cross-
sectional survey of event visitors. Different age
groups were considered as were casual visitors and
so-called expert visitors who attend events on a regu-
lar basis. Even though the participants were obligated
to own a smartphone, less technophilic participants
were as important as more experienced users.
Twenty-one participants started the field study of
whom 16 terminated successfully after two weeks by
completing the survey. Of the 16 participants who
completed the study, one was younger than 18 years
old, three were between the ages of 18 and 25, nine
were between 26 and 35, two were between 36 and
45 and one was older than 45 years old. The aver-
age technical affinity of the participants is 3.73 (σ:
0.88) on a 5-point Likert scale according to their own
estimation. Mobile event applications were used by
62.5% of the participants once a month or less often
and by 18.8% at least once a week. The participants
reported different experiences with recommender sys-
tems such as the Amazon website. Recommender
systems were used several times per week by 25% and
43.8% used them once per month or less often. About
a third of the participants, 37.5%, can be called expert
visitors as they attend events requiring a ticket pur-
chase once per month or more often whereas 62.5%
purchase tickets not more often than a few times per
5.3 Results
The responses (Figure 4) show that the participants
are satisfied with our solution (: 3.75, σ: 0.83).
Only 25% of the participants rated the system with a
3 or less. We also wanted to know if the received rec-
ommendations met their expectations. The responses
are slightly above average (: 3.38, σ: 0.60). Ac-
cording to the survey results, the recommendations
can be called sufficiently diversified (: 3.63, σ:
1.05). The participants believe that their feedback im-
proves the personalized recommendations (: 3.69,
Figure 4: Overall satisfaction with the recommender sys-
Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations
σ: 0.77). The frequency of usage differed between
participants. The 16 participants would have bought
a total of 16 tickets based on the recommendations
which means one sold ticket per person during the
two week study period. A majority of the participants,
87.5%, mentioned that they would like to continue us-
ing the system to find interesting events in the future.
In general, the users were satisfied with the choice
of settings which allowed them to modify the context-
awareness (: 3.5, σ: 0.79). They particularly liked
how they were able to influence the geographical con-
text (: 3.94, σ: 0.75). A higher variance of ratings
can be observed when asking the participants about
the calendar view (: 3.56, σ: 1.17) and the list of
genres (: 3.44, σ: 1.12). A free text field at the
end of the survey allowed the participants to add fur-
ther thoughts. Some participants requested further op-
tions to modify the context-awareness, for example,
the user’s current budget for events.
The developed Android application is only one ex-
ample of how our algorithm can be implemented in a
mobile event recommender system. According to the
participants, the apps’ user interfaces are intuitive (:
4.06, σ: 0.97) and they were pleased with the chosen
card layout for the new recommendations (: 4.07,
σ: 0.77).
5.4 Discussion
Our field study allowed us to evaluate the recommen-
dations generated by our context-aware CBCF algo-
rithm in a realistic scenario. The results show that
our approach to adapt CBCF is a promising way to
generate personalized event recommendations. The
user’s feedback on past recommendations is a valu-
able basis to fill the user-item rating matrix which
can be extended by a CB prediction. The CF algo-
rithm based on this matrix is able to provide accurate
recommendations and ensures a sufficient diversity of
events. Furthermore, our system is able to provide
recommendations even if only one user is using the
CBCF can be extended by context-aware recom-
mendations, another important finding of the study.
The recommendations do not only satisfy the search
activities of the users, but the participants in our sur-
vey were also pleased they were able to influence the
context-awareness. Nevertheless, our study reveals
that further context factors should be identified and
considered during the recommendation process. Our
first prototype focuses on the most obvious context
factors such as the user’s current location or tempo-
ral context factors. In addition to the price of the
event, further context factors which were completely
ignored during this work should be incorporated in
order to improve the quality of the recommendations
in different situations. Examples of such factors are
the current weather or if the user is alone or accom-
panied by someone. In this case, a group recom-
mender could help to find suitable recommendations
for all group members. If, for example, a fan of music
events is accompanied by her or his children, recom-
mending a child-friendly event will be more appro-
priate than a visit to a concert. While our first pro-
totype mainly supports users in receiving recommen-
dations at a certain time before the actual event takes
place, we believe that additional, contextual informa-
tion would also allow to find suitable recommenda-
tions even more spontaneously (Herzog and Woerndl,
According to the feedback we got from our par-
ticipants, we learned that the users of mobile rec-
ommender systems value appealing user interfaces.
The chosen card layout for new recommendations re-
ceived positive feedback, confirming our belief that
this layout is an appropriate way to present recom-
mendations and to offer a quick and easy solution for
providing feedback at the same time.
In comparison to movie recommendations, research
has paid little attention to recommender systems for
events. Minkov et al. (2010) present an approach
which considers the individuals’ preferences for past
events and combines these preferences with other
peoples’ likes and dislikes. Dooms et al. (2011) con-
ducted a user-centric evaluation of different event rec-
ommender algorithms. Results show that a hybrid
of content-based and collaborative filtering performs
better than other algorithms in most quality factors
like accuracy, satisfaction and usefulness. The hy-
brid approach of Khrouf and Troncy (2013) combines
content-based and collaborative filtering and is en-
riched by Linked Data to overcome data sparsity. Cor-
nelis et al. (2005) model user and item similarities
as fuzzy relations in their hybrid approach. Outlife
is an event recommender which groups friends in so-
cial groups (De Pessemier et al., 2013). It allows the
creation of recommendations for specific groups of
friends and can recommend friends who should be in-
vited to an event. Zhang et al. (2013) present a group
recommender which recommends event-based groups
using matrix factorization. A study using Meetup.com
data shows the effectiveness of their approach. Quer-
cia et al. (2010) analyzed mobile phone location data
to understand how the user’s current location influ-
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
ences the acceptances of social event recommenda-
tions. They found out that recommending events that
are popular among residents of an area is more bene-
ficial than recommending nearby events. This finding
can be seen as a promising solution for the Cold-Start
Problem in event recommender system. If the event
is location independent, Daly and Geyer (2011) sug-
gest using the popularity as a metric for overcoming
the Cold-Start Problem. A special type of events that
can be recommended are distributed events. These
events are collections of smaller, single but very sim-
ilar events that occur at the same day (Schaller et al.,
A few mobile applications offering personalized
event recommendations are already on the market.
One example is Bandsintown, an application available
for Android and iOS devices which focuses on music
events (Figure 5). Recommendations made for events
take into consideration music selections locally stored
on the user’s device. Furthermore, external sources
like Facebook or Twitter can be connected to the ap-
plication in order to collect additional information
about user preferences in regard to music. Other ex-
amples of mobile applications which offer some kinds
of event recommendations are the EVENTIM DE ap-
plication, offered by the German ticketing and event
company CTS Eventim and XING EVENTS, an ap-
plication of the German based social network for pro-
fessionals XING. The fact that the ticketing and event
industry has already started to implement personal-
ized recommendations in their offers underlines the
significance of the topic for the different players in-
volved in this industry. Visitors, on the one hand, can
find interesting events nearby which they might over-
look otherwise. On the other hand, the recommen-
dations promise additional sales and the users’ feed-
back allows organizers to predict the number of fu-
ture visitors for similar events (Minkov et al., 2010).
Setten et al. (2004) focus on context-aware sys-
tems. The mobile application COMPASS demon-
strates how user interests and context can be respected
in a recommender systems. Oku et al. (2006) show
that recommendations which take the user’s context
into consideration lead to a higher user satisfaction. A
general model for context-aware and proactive recom-
mending is delivered by Woerndl et al. (2011). Ado-
mavicius and Tuzhilin (2011) emphasize the impor-
tance of context in recommender systems and intro-
duce three different paradigms for incorporating con-
textual information into the recommendation process.
The related work shows the potential of event rec-
ommender systems and the advantages of context-
awareness. But to the best of our knowledge, there
is no work examining the use of CBCF within a
Figure 5: Bandsintown Android application.
context-aware recommender to recommend all kinds
of events. We closed this gap by showing that CBCF
can be extended by context-aware recommendations
and adapted to the scenario of event recommenda-
tions. Our results show that the chosen approach is
able to deliver promising recommendations.
In this paper, we introduced a new method for per-
sonalized event recommendations. We developed an
algorithm based on CBCF and extended it by context-
aware recommendations. This algorithm was imple-
mented in an Android application and evaluated in a
two-week field study. According to the results, our
approach delivers promising recommendations. By
combining different recommendation techniques, cer-
tain limitations, such as the New Item Problem and a
lack of diversity, can be overcome. We believe that
the results of the field study emphasize the economic
potential of the developed system and underline the
importance of mobile recommender systems in the
ticketing and event industry. The first applications of
recommender systems that provide some kind of per-
sonalized recommendations are already available and
are steadily gaining popularity.
Future work should focus on identifying addi-
tional context factors to be considered during the al-
gorithm execution. These factors should be capable
Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations
of finding suitable recommendations in different situ-
ations, e.g., for tourists who spend only a very short
time visiting a city. Improvements are also possible
in regards to other solutions of context incorporation.
Currently, we use pre-filtering only, but post-filtering
and contextual modeling should be considered as well
in future work. The contextual pre-filtering immedi-
ately excludes inappropriate events but we should ex-
amine if users are willing to accept little violations of
the thresholds if an event really satisfies their needs.
Furthermore, social recommendations promise an im-
provement of the recommendations. Events of par-
ticular importance for the user, e.g., events the user
liked on social networks like Facebook, could receive
a bonus on the calculated rating to increase the prob-
ability of recommendation. Another aspect is implicit
user feedback which was not collected by this first
prototype but could be used to improve the recom-
mendations. Our first prototype or pure CB or pure
CF approaches could be used as a baseline application
in order to evaluate the suggested extensions and their
influence on the quality of the recommendations. For
this purpose, we want to conduct another study with a
larger number of users which allows us to obtain more
in-depth user feedback and statistics.
This work is part of the TUM Living Lab Connected
Mobility (TUM LLCM) project and has been funded
by the Bayerisches Staatsministerium f
ur Wirtschaft
und Medien, Energie und Technologie (StMWi).
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Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations