The Information Value of Context for a Mobile News Service
Toon De Pessemier, Kris Vanhecke and Luc Martens
iMinds - WiCa, Ghent University, Gaston Crommenlaan 8 box 201, B-9050 Ghent, Belgium
Keywords:
Recommender System, News, User Study, Context, User Interaction.
Abstract:
Traditional recommender systems provide personal suggestions based on the user’s preferences, without tak-
ing into account any additional contextual information such as time or device type. However, in many ap-
plications, this contextual information may be relevant for the human decision process, and as a result, be
important to incorporate into the recommendation process, which gave rise to context-aware recommender
systems. However, the information value of contextual data for the recommendation process is highly depen-
dent on the application domain and the users’ consumption behavior in different contextual situations. This
research aims to assess the information value of context for a recommender system of a mobile news service by
analyzing user interactions and feedback. A large-scale user study shows that context-aware recommendations
outperform traditional recommendations, but also indicates that the accuracy improvement might be limited
in a real-life situation. Service usage takes place in a limited number of different contexts due to user habits
and repetitive behavior, leaving little room for optimization based on the context. Data fragmentation over
different contextual situations strengthens the sparsity problem, thereby limiting the user-perceived accuracy
gain obtained by incorporating context in the recommender. These findings are important for news providers
when considering to offer context-aware recommendations.
1 INTRODUCTION
Recommender systems are software tools and tech-
niques providing suggestions for items to be of in-
terest to a user such as videos, songs, or news arti-
cles. The consumption of these audiovisual media and
the accessing of information always happen in a cer-
tain context (Ricci, 2012), i.e. conditions or circum-
stances that significantly affect the decision behavior.
This gave rise to the development of context-aware
recommender systems (CARS), which take this con-
textual information into account when providing rec-
ommendations.
For various application domains, the user con-
text has gained an increased interest from re-
searchers (Adomavicius and Tuzhilin, 2011). For
context-aware music recommendations for example,
the user’s emotions can be used as input by using
support vector machines as emotional state transi-
tion classifier (Han et al., 2010). In the applica-
tion domain of tourism for example, various applica-
tions use the current location or activity of the user to
personalize and adapt their content offer to the cur-
rent user needs (Ricci, 2010; De Pessemier et al.,
2014). Personal recommendations for points of in-
terest can be provided based on the user’s proximity
of the venue (Kenteris et al., 2010).
In the domain of audiovisual media, more specif-
ically news content, the influence of context on the
consumption behavior and personal preferences is
less obvious. However, research (Yu et al., 2006) has
shown that the situation of the user (location, activ-
ity, time), as well as the device and network capabil-
ities are important contextual parameters for context-
aware media recommendations on smartphones.
The growth of the digital news industry and espe-
cially the development of mobile products is boom-
ing. Mobile has become, especially amongst younger
media consumers, the first gateway to most online
news brands. In a recent survey (Reuters Institute
for the Study of Journalism, 2014), conducted in 10
countries with high Internet penetration, one-fifth of
the users now claim that their mobile phone is the pri-
mary access point for news.
Despite this shift of news consumption to the mo-
bile platform, the study of Weiss (Weiss, 2013) high-
lights that a gap exists between what news consumers,
particularly young adults, are doing and using on their
smartphones and what news organizations are able to
provide. In most cases, news organizations disregard
contextual data or they are only using geo-location
features in their mobile apps for traffic and weather;
363
De Pessemier T., Vanhecke K. and Martens L..
The Information Value of Context for a Mobile News Service.
DOI: 10.5220/0005408603630369
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 363-369
ISBN: 978-989-758-106-9
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
they do not anticipate the high use of location-based
services by smartphone consumers.
2 RELATED WORK
In the domain of digital news services, various ini-
tiatives to personalize the offered news content have
been proposed.
SCENE (Li et al., 2011) is such a news ser-
vice. It stands for a SCalable two-stage pErsonalized
News rEcommendation system. The system consid-
ers characteristics such as news content, access pat-
terns, named entities, popularity, and recency of news
items when performing recommendation. The pro-
posed news selection mechanism demonstrates the
importance of a good balance between user interests,
the novelty, and diversity of the recommendations.
The News@hand system (Cantador et al., 2008)
is a news recommender which applies semantic-based
technologies to describe and relate news contents and
user preferences in order to produce enhanced rec-
ommendations. This news system ensures multi-
media source applicability and multi-domain porta-
bility. The resultant recommendations can be adapted
to the current context of interest, thereby emphasizing
the importance of contextualization in the domain of
news. However, context is not the main focus of this
study and the influence of context on the consumption
behavior is not investigated.
The News Recommender Systems Chal-
lenge (Said et al., 2013) focused on providing
live recommendations for readers of German news
media articles. This challenge highlighted why news
recommendations have not been as analyzed as some
of the other domains such as movies, books, or music.
Reasons for this include the lack of data sets as well
as the lack of open systems to deploy algorithms
in. In the challenge, the deployed recommenders
for generating news recommendations are: Recent
Recommender (based only on the recency of the
articles), Lucene Recommender (a text retrieval sys-
tem built on top of Apache Lucene), Category-based
Recommender (using the article’s category), User
Filter (filters out the articles previously observed
by the current user), and Combined Recommender
(a stack or cascade of two or more of the above
recommenders).
Although the various initiatives emphasize the im-
portance of a personalized news offer, most of them
focus on the recommendation algorithms and ignore
the contextual information that is coupled with the in-
formation request, the user, and the device. In this
study, the focus is not on improving state of the art
recommendation algorithms, but rather on investigat-
ing the influence of context on the consumption of
news content by means of a large-scale user study.
In many cases, the research on CARS remains
conceptual (Adomavicius and Tuzhilin, 2011), where
a certain method has been developed, but testing is
limited to an offline evaluation or a short-term user
test with only a handful of people, often students or
colleagues who are not representative for the popula-
tion. In contrast, this research investigates the role of
context for news recommendations, based on a large-
scale empirical study. Users could utilize a real news
service
1
that offers content of four major Flemish
news companies on their own mobile devices, in their
everyday environment, where and when they wanted,
i.e., in a living lab environment.
Living lab experiments are an extension towards
more natural and realistic research test environ-
ments (Følstad, 2008). Living labs allow to evalu-
ate research hypotheses by users representative for
the target population who satisfy their information
need in a real context. Since users are following
their own agenda, laboratory biases on their behav-
ior can be neglected (Hopfgartner et al., 2014). Al-
though less transparent and predefined, living lab ex-
periments aim to provide more natural settings for
studying users’ behavior and their experience.
Especially for context-aware applications, in
which the user’s environment has an influence on the
way the application works and/or on the offered con-
tent, a realistic setting is essential for a reliable eval-
uation. Therefore, this paper investigates the influ-
ence of context and the benefit of context-aware rec-
ommendations for a real news service by means of a
large-scale user panel, in a realistic environment, over
a longer period of time. Since a user study can pro-
vide reliable explanations as to which recommenda-
tion method is the best, and why one method is better
than the other (Shani and Gunawardana, 2013), three
alternative recommendation methods for the news ser-
vice are compared through such a user study.
3 EXPERIMENTAL SETUP
The news service that was used in this experiment
1
,
aggregates content of different premium content
providers: newspapers, magazines, but also content
of television as short video clips. Figure 1 shows a
screenshot of the user interface offering the content
with a reference of the provider on top of each con-
tent item. The aggregated content provides users a
1
http://www.iminds.be/en/projects/2014/04/17/
stream-store
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364
more complete and varied overview of the news than
traditional services do. To anticipate the abundance
of news content and the associated choices that peo-
ple have to make, the news service offers personalized
recommendations.
During the experiment, the device type (smart-
phone or tablet) and the time of the day (morning,
noon, daytime, or evening) are studied as contextual
influences of the news consumption. The news ser-
vice is accessible through a mobile application, which
is available on Android and iOS for tablets as well as
smartphones. As a result, the type of device that is
used for consuming the news content is an interesting
contextual factor.
Compared to the well-established application do-
mains of recommender systems, such as movies or
books, news items have a shorter lifespan and are fre-
quently updated. Consequently, consulting the news
on a daily basis, or even multiple times a day, can be
interesting, which makes the time aspect another im-
portant contextual factor. The time is closely related
to the location of the user, as was also witnessed dur-
ing the analysis of users’ interactions and consump-
tion behavior. A frequently recurring pattern was as
follows: in the morning, users are at home; during
daytime, they are at work; and during the evening,
they are again at home. Therefore, and to prevent over
specification (Section 4.3), the location is not adopted
as a separate contextual factor.
For the evaluation of this service and its recom-
mendations, 120 test users were recruited by an ex-
perienced panel manager from iMinds-iLab.o
2
(i.e. a
research division with a strong expertise in living lab
research and panel management). These test users, all
interested in news and owning a smartphone and/or
tablet, belong to the target group of an online news
service. The test users could install the mobile appli-
cation of the news service on their smartphone and/or
tablet and freely use the service during the evalua-
tion period of around 5 weeks. These test users were
divided into three groups, each receiving a different
type of recommendations, as explained in Section 4.
The test users’ interactions with the service were
logged to analyze their consumption behavior and to
get insight in the actual use of the news service and
their overall experience: 10 test users did not install
the app, or did not use the news service during the
evaluation period. They are excluded from the analy-
sis, so that the number of actual participants was re-
duced to 110.
2
http://www.openlivinglabs.eu/livinglab/iminds-ilabo
4 NEWS RECOMMENDATIONS
The experiment takes three different approaches to
recommend interesting news. Each user received only
one recommendation type during the whole evalua-
tion period. To avoid any bias, test users were not
informed about the existence of multiple types of rec-
ommendations.
4.1 Recommendations based on Explicit
Static Preferences
Before the actual experiment, test users were asked
about their preferences for different categories of
news content (National, International, Culture, Econ-
omy, Lifestyle, Politics, Sports, and Interesting facts)
through an online questionnaire. Users could specify
their interests on a 5-point rating scale for each cate-
gory and refine this score for different times of the day
(morning, noon, daytime, and evening). The answers
on this questionnaire constitute the user profile that
is used for generating news recommendations. Dur-
ing the experiment, these preferences are considered
static; user profiles are not updated based on explicit
or implicit feedback on the content, and the recom-
mender is not learning from the user’s behavior.
4.2 Content-based Recommendations
The content-based recommendations are not based on
a prior questionnaire but use the implicit and explicit
feedback users provide during the evaluation period.
A request to read one of the recommended news items
is considered as positive implicit feedback. Eval-
uating the news recommendation by means of the
‘Thumbs Up’ and ‘Thumbs Down’ icons in the user
interface provides explicit feedback.
This feedback is gradually collected during the
usage of the service. As a result, the user profiles
of the content-based recommender are dynamic and
constantly change as users interact with the news ser-
vice. As the user is utilizing the news service and
provides feedback, the recommender is learning the
user’s preferences.
A content-based recommendation algorithm was
chosen because of the availability of informative
metadata about the content items, the sparsity of the
data set, and the cold start problem associated with the
start-up phase. As content-based solution, the ‘Inter-
estLMS algorithm’ of the Duine framework (Telem-
atica Instituut / Novay, 2009) was adopted. The In-
terestLMS algorithm builds a user profile by inferring
personal preferences from the metadata describing the
news items that are requested (implicit feedback) or
TheInformationValueofContextforaMobileNewsService
365
Figure 1: Screenshot of the user interface of the news service.
evaluated (explicit feedback). Subsequently, the algo-
rithm determines the news items that best match the
user’s profile.
News items are characterized by eight different
categories, the same as used in the questionnaire to
elicit the explicit static preferences of the users. In
addition, keywords provide more detailed info about
the news items. These keywords are not predefined
but are extracted from the text of the article using
OpenCalais (Thomson Reuters, 2013). OpenCalais is
a Web service that automatically creates rich semantic
metadata for the content. It analyzes the news article
and finds the entities within it, but it returns the facts
and events hidden within the text as well. This way,
the news article is tagged and analyzed with the aim
of checking whether it contains information what the
user cares about.
For the content-based recommendations, contex-
tual aspects are not taken into account. So, contextual
data, such as the device type and the time of the day,
are ignored during the creation of the profile and the
calculation of the recommendations.
4.3 Context-aware Content-based
Recommendations
Just like the content-based recommendations, the
context-aware content-based recommendations are
not using a prior questionnaire but are self-learning
based on the explicit and implicit feedback users pro-
vide during the experiment. For this type of recom-
mendations, the InterestLMS algorithm of the Duine
framework is extended to take into account the con-
text of the user. Before generating the recommenda-
tions, the user feedback is processed by a contextual
pre-filter (Adomavicius and Tuzhilin, 2008). Contex-
tual information is used to determine the relevance of
the feedback and filter these data based on the cur-
rent situation. For instance, if a user wants to read
news during the evening, an exact pre-filter (Ado-
mavicius and Tuzhilin, 2011) selects only feedback
gathered during the evening to calculate the recom-
mendations. Therefore, the day is partitioned into
four non-overlapping intervals: morning from 6:00 to
11:00, daytime from 11:00 to 12:00 and from 13:00
to 18:00, noon from 12:00 to 13:00 and evening/night
from 18:00 to 6:00.
One major advantage of the contextual pre-
filtering approach is that it allows deployment of any
of the traditional recommendation techniques (Ado-
mavicius and Tuzhilin, 2005). This makes it pos-
sible to use the same underlying algorithm for the
context-aware content-based recommendations as for
the content-based recommendations, which enables
the comparison of both types of recommendations and
to investigate the influence of contextual information.
Different pre-filtering techniques have proven
their efficacy in literature (Baltrunas and Ricci, 2009).
They all have to cope with the problem of context
over-specification: focusing on the exact context is of-
ten a too narrow limitation. An overly specified con-
text may not have enough training examples for accu-
rately estimating the user’s interests. For example, if
a user rarely utilizes a tablet during noon to read news
articles, the exact context (noon + tablet) may not pro-
vide enough data (feedback from the user) for accu-
rately calculating the recommendations, which gives
rise to the ‘sparsity’ problem. As a result, insufficient
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feedback is available for generating reliable recom-
mendations (Papagelis et al., 2005).
An appropriate solution for context over-
specification is to use a more general context
specification by applying context generaliza-
tion (Adomavicius and Tuzhilin, 2011). Since certain
aspects of the overly specific context may be less
significant, the data filtering can be made more
general in order to retain more data after the filtering
for calculating recommendations.
In this experiment, context generalization is ap-
plied in two phases in case of insufficient feedback
data. In a first phase, the time frame is broadened. For
instance, if recommendations are needed for a user
who is reading news on a tablet during noon, the time
restriction “noon” is dropped first. The data gathered
in that specific context is supplemented with the user’s
feedback gathered on a tablet during other time peri-
ods. If the amount of feedback is still insufficient after
this first generalization, the context is further gener-
alized. In a second phase, the device type is broad-
ened. More specifically, the user’s feedback provided
on a specific type of device (e.g., a tablet) is supple-
mented with the user’s feedback provided on other de-
vice types (e.g., a smartphone). We opted to apply the
generalization first on the time aspect of the context,
and in a second phase on the device type, since many
users are utilizing the service during different time pe-
riods but on only one type of device.
5 FEEDBACK ANALYSIS
To quantify the added value of a dynamic profile
and contextual information, the users’ interactions
in terms of feedback with each of the three recom-
menders are analyzed. Figure 2 gives an overview of
the user feedback on the news service during the eval-
uation period. The chart distinguishes implicit feed-
back, i.e. requesting to ‘view’ a news item, and ex-
plicit feedback, i.e., evaluating a news item by pro-
viding a ‘Thumbs Up’ or ‘Thumbs Down’ rating.
In Figure 2, this user feedback is aggregated over
all users and partitioned by the type of recommenda-
tions that the users received. Since some test users
dropped out just before the evaluation period, the dif-
ferent recommender types are not evaluated by the
same number of test users. Table 1 shows the number
of test users assigned to each type of recommenda-
tions, which has a direct influence on the total amount
of feedback gathered for that type.
During the evaluation period, 2728 positive eval-
uations (‘Thumbs Up’) of a news recommendation
were registered for all types of recommendations
Figure 2: The amount of user interaction with the news ser-
vice for each type of recommendations, partition according
to the type of interaction.
together. In contrast, only 949 times a negative
evaluation (‘Thumbs Down’) was provided by the
users. These aggregated values (‘Thumbs Up’ 74.2%
- ‘Thumbs Down’ 25.8%) are an indication for the
general satisfaction of the users with the news that
they get recommended.
However, significant differences for the different
types of recommendations can be witnessed. Com-
pared to the recommendations that are based on the
explicit static preferences of the users, less views (to-
tal number, but also number per user) are obtained for
the content-based and context-aware content-based
recommendations. This might indicate that users get
the interesting news more quickly using the more ad-
vanced algorithms, since they also spent more time
per news item.
Comparing the different types of recommenda-
tions in terms of negative feedback (‘Thumbs Down’)
demonstrates the added value of personal feedback
and the context of the user for the recommender sys-
tem. Recommendations based on explicit static pref-
erences received 411 times ‘Thumbs Down’ from
users who are not satisfied with the news content. The
content-based recommender uses the implicit feed-
back (requests to view a news item) and explicit eval-
uations (‘Thumbs Up & Down’) as personal feedback
during the evaluation period. Compared to the recom-
mendations based on explicit static preferences, less
negative (309 times ‘Thumbs Down’) and more pos-
itive evaluations are provided for the content-based
recommendations. The lowest number of negative
evaluations (229 times ‘Thumbs Down’) was received
for the context-aware content-based recommender,
which suggest news items based on the personal feed-
back of the user and by taking into account the user’s
current context. A Wilcoxon rank-sum test showed
these differences are significant (p = 0.043 < 0.05).
Table 1 shows the ratio of the number of positive
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367
Table 1: Comparison of the recommendation types.
Recommender Type Explicit Static Preferences Content-based Context-aware Content-based
Input Data Preliminary Questionnaire Personal Feedback Personal Feedback + Context
Number of Test Users 38 37 35
#Thumbs up / #(Thumbs up + down) 0.67 0.75 0.79
evaluations (# Thumbs Up) and the number of explicit
evaluations (# Thumbs Up + Down) for the different
types of recommendations. The results confirm the
increase in accuracy by making the system dynamic
(content-based recommendations) and taking into ac-
count the context (context-aware content-based rec-
ommendations).
6 DISCUSSION
Although the context-aware content-based recom-
mendations received the most positive evaluation
(highest ratio in Table 1), the difference with the tradi-
tional content-based recommendations is limited be-
cause of two reasons.
Firstly, many test users are not active in differ-
ent contextual situations. They tend to read the news
each day at the same time, using the same device. In
this study, 10 users (9.1%) utilized the service dur-
ing only one time period (morning, noon, daytime or
evening), 17 users (15.5%) have viewed news items
during two different time periods, 36 users (32.7%)
were active during three different time periods, and
47 users (42.7%) have requested news content during
morning, noon, daytime, and evening (all four time
periods). In terms of time period, users were far most
active during the evening (45.0% of the news request),
followed by the morning (26.7%). Over the evalua-
tion period of 5 weeks, only 47 users (42.7%) utilized
the service at least once during all four time periods
(morning, noon, daytime, and evening).
In terms of device types, 91 users (82.7%) uti-
lized only one device to access the news content (ei-
ther smartphone or tablet) and only 19 users (17.3%)
used both devices at least once for reading news. In
addition, users that used both device types may have
the habit or preference to read news on only one of
them. On the smartphone, news is more often con-
sumed during different times of the day (76.8% used a
smartphone during 3 or 4 time periods) than on tablets
(56.7% used a tablet during 3 or 4 time periods). Peo-
ple generally keep their phone more closely through-
out the whole day than their tablet, which is often used
only once during the day, typically at home. Because
some users consume news in fixed usage patterns,
with limited variations in their context, the context-
aware recommender cannot fully exploit the contex-
tual information for this news service.
Secondly, the results are based on the evaluation
period of approximately 5 weeks, and CARS require
sufficient time to learn user preferences in different
contextual situations. Because of the cold start prob-
lem (i.e. the issue that the system cannot draw any
inferences for users or items about which it has not
yet gathered sufficient information) and data fragmen-
tation over the different contextual situations, we be-
lieve that there is still room for accuracy improvement
of the context-aware content-based recommendations
by gathering additional user feedback over a longer
time period. The required number of ratings to over-
come the cold start problem depends on various fac-
tors such as the algorithm parameters, the content do-
main, and the specific items that are rated. Studies
have shown that, in general, more than 20 - 30 ratings
are necessary for the system to recommend relevant
items to the user (Lee et al., 2007). In our user study,
some of the users did not achieve enough ratings for
each contextual situations.
Additional data can help to learn patterns in the
users’ behavior and preference differences for vari-
ous contextual situations, thereby further improving
the accuracy of the context-aware content-based rec-
ommendations.
7 CONCLUSIONS
In this paper, a start-up news service offering personal
recommendations is evaluated by an empirical user
study. Three types of recommendations are tested:
recommendations based on an explicit static pro-
file, content-based recommendations using the actual
user behavior but ignoring the context, and context-
aware content-based recommendations incorporating
user behavior as well as context. The study aimed to
assess the importance of context in the recommender
of a real-life mobile news service by focusing on two
contextual aspects: device type and time. The results
confirm the added value of contextual information for
the recommender, but also indicate that the accuracy
improvement might be limited in a real-life situation.
Two conditions must be met in order to improve tra-
ditional recommenders with contextual information.
Firstly, users have to utilize the service in different
contextual situations. Logged user behavior showed
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that users typically access the news service with only
one device (either smartphone or tablet) at different
times of the day, but especially in the evening. Sec-
ondly, sufficient training data have to be available to
learn user preferences and variations in these prefer-
ences over different contextual situations. In context-
aware recommender systems, the cold start problem
is strengthened by the fragmentation of the consump-
tion data over different contextual situations. Context
generalization can be a partial solution, but choosing
the right contextual aspect to optimally broaden the
context is often difficult.
ACKNOWLEDGEMENTS
This research was performed in the context of the
iMinds-MIX Stream Store project, which is cofunded
by iMinds, a research institute founded by the Flem-
ish Government.
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