Social Activity as the New TV Guide
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Social Activity as the New TV Guide
Keith Mitchell1/2, Nicholas Race1/2, Adam Lindsay2
1 2
21media innovations ltd, InfoLab21, Lancaster, UK; School of Computing and Communications, Lancaster University, Lancaster, UK
E-mail: keith@21media.tv, race@comp.lancs.ac.uk, atl@comp.lancs.ac.uk
Abstract: The huge catalogues of video items currently nation during the televised Leaders’ Debate. Our recent
available through live, on-demand and catchup TV research is also exploring this direction and specifically
based services coupled with the ever expanding range looks to providing awareness of friend activity within a
of online video services (Vimeo, YouTube, Netflix) social network and exposing this on a TV service user
provides users with unprecedented levels of choice. interface. Ultimately, we seek to help the user answer the
Not only is the choice of content expanding but so too key question “What should I watch now”? to achieve this
are the range of formats, delivery mechanisms and the we expose information relating which helps to answer
viewing devices (PC, TV/STB, mobile device). In order additional questions viewers have which are “what live
to navigate these repositories sensibly recommender content is available now?”, “is there anything I may have
systems provide a key role in helping a user filter missed?” and “what are my friends watching now or what
items of interest. In this paper, we describe our initial have the watched recently?”.
work on integrating an IPTV service with social The remainder of this paper describes the social IPTV
networks in order to support the personalisation system developed and which has been operational at the
process by exploiting the social graph. University of Lancaster, UK for 9 months. We outline
some initial results and conclude by defining a number of
Keywords: Social TV, IPTV, Recommender Systems, key areas of future work which need to be addressed by
Social Graph, Content Distribution the research and commercial communities.
1 INTRODUCTION 2 RELATED WORK
Traditionally, recommender systems (RS) track user
The Internet is increasingly being used to distribute both
activity and suggest suitable alternatives to each
real-time and on-demand high bandwidth multimedia
individual viewer based on their previous activity. A
content to large audiences. IPTV specifically, through
number of previous (and existing) systems that
services like BBC iPlayer, Hulu, TV Catchup, is on the
recommend TV shows or movies using this technique
increase and traditional ‘over the air’ broadcasters are
include, Netflix and LoveFilm which are both online TV
now looking to new delivery mechanisms via the Internet.
provider and DVD rental services, WeOnTV [1]. Activity
These new delivery mechanisms offer flexible viewing
in these contexts is normally based on explicit user
for the end user. Users are increasingly able to watch
activity, such as rating, voting, commenting. However, it
what they want, when they want and within IPTV systems
is well understood that the majority of viewers are
where content is increasingly being delivered to a browser,
consumers of content and not necessarily contributors, so
users are also able to view related web content
one may argue that it is not reliable to exploit these
simultaneously. In these scenarios where the linear
factors alone.
broadcast model increasingly gives way to on-demand
access, there is currently a limited understanding of the As a brief overview, in general, recommender algorithms
emerging usage patterns and context of use. recommend items similar to the ones the user liked in the
past and can be classified into two families:
We believe that a better understanding of the consumption
patterns are required in order for recommendation • content-based filtering CBF) finds similarity between
systems to accurately recommend suitable content to end- items on the basis of the items content (e.g., gender,
users. Additionally, we believe that traditional content- director, actors, plot)
based filtering and collaborative filtering techniques • collaborative filtering (CF) finds similarity between
would benefit from the use of activity information items on the basis of collaborative information about
gleaned from social networks, which essentially represent users, i.e., they use the opinions (known as ratings)
the human network. It is well understood and expressed by the community of ITV users.
acknowledged that TV viewers like to discuss the shows The most popular recommender systems are based on
they watch, so called water cooler conversations. A collaborative filtering, the biggest advantage over
number of system have been developed which encourage content-based systems being that collaborative filtering
real-time discussion and interaction and in the United relies only on explicit or implicit opinions expressed by
Kingdom, the 2010 Election led to Broadcasters users while explicit content description are not required.
experimenting with real-time video delivery on the web
However, in order to accurately provide recommendations,
which also showed Twitter and Facebook feeds so that
collaborative systems must derive users’ preferences
viewers could comment and observe the mood of the
Keith Mitchell, 21media innovations ltd, KBC, InfoLab21, Lancaster, LA1 4WA, +44 (0)161 498 5860, keith@21media.tvtoward items, which is done through analysis of viewers’ operational since November 2009 at the University of
past interaction with the TV programmes. The RS must Lancaster, UK with over 6,500 students on campus.
thus track what the user are watching, in other words it
must collect viewers’ ratings. However, collaborative 3.1 Hardware Architecture
filtering requires large numbers of ratings before they can The backend hardware includes three IPTV gateway units
make reasonable suggestions back to the community and (also referred to as a Head End), a single Network
these systems additionally suffer from ‘cold-start’ Attached Storage (NAS) drive, a web server and a
problem which refers to situations where there are only database server. To provide a live channel stream, digital
few ratings to base recommendations on. terrestrial (DVB-T) and free-to-air satellite (DVB-S) are
Our initial work in this area is looking into implicit taken off-air and fed directly to each of the three IPTV
‘interest profile generation’ which includes data such as gateway units. Each gateway then multicasts individual
linger and viewing times, channels changes/requests, and MPEG-2/H.264 encoded channels over the University’s
in fact any interaction with the TV based service. The private core and residences network. To serve the web-
focus of our work is on understanding the user-journey in based client an Apache HTTP server with PHP is
detail and moving away from focusing on a specific employed to provide a mixture of dynamic AJAX-enabled
content type (live or on-demand), genre or specific event, HTML pages and XML feeds in order to build the client
in order to develop more generalisable solutions. A and its supporting pages, as described in [7].
distinguishing element of the work presented here is the 3.2 Client Interface Components
development of our own measurement platform which
accurately tracks the user journey (per click or event) The web based interface is shown in Figure 1 and consists
irrespective of the context of use, e.g. VoD or live streams. of several components including the primary navigation
A further novel aspect of our approach is to monitor and menu enabling a user to choose between live TV, radio
investigate the impact of the social graph (gleaned from and VoD content, the video window based on the VLC
social networks such as Facebook) and to understand its media player plug-in, the content carousel, filters for
potential influence on patterns of access. reordering the content carousel and the social panel, for
showing information retrieved from social networks.
This contrasts previous work which tends to rely either on
passively collected traces, publicly available traces of
static content (e.g. Internet Traffic Archive [5]), or
privately obtained (anonymised) logs of more dynamic
streaming content from larger networks such as Akamai’s
[11]. In Hei et al [6] for example, the performance of
PPLive using traces collected through passive packet
sniffing is studied. In [10], Silverstone et al in use a
similar approach to compare PPLive, PPStream, SopCast,
and TVAnts data collected during the FIFA World Cup in
2006. In general, the aforementioned works could be
regarded as providing “black-box or out-of-band”
measurement analysis as they do not instrument the end- Figure 1: Wireframe highlighting UI design
user client application itself.
Figure 2 shows the real web interface with EPG data
Within the broadcast Television domain, Social TV being shown to the user along with a live video feed and
currently refers to the ability to allow people tweets related to the video content obtained via Twitter.
geographically separated but watching the same program From here, a user may hover their mouse over a
to feel as if they were co-located and having a shared programme title to view a synopsis of the programme or
experience [1]. These systems tend to be before the mass hover over a channel logo to display channel-specific
popularity of social networks such as Facebook, Bebo and Facebook-related social data, namely who else is
Twitter. Social TV increasingly refers to the integration watching right now or watched recently.
of TV services with social networks such as Channel 4’s
Test Tube Telly allowing users to broadcast the
programme they are watching to Facebook and Twitter.
IPTV services such as Hulu and Zatoo provide some basic
social features in that they offer direct communications
channels between viewers [1]. However, as yet, none of
these leverage the social graph to support the knowledge
acquisition and decision making processes.
3 SOCIAL TV
This section describes briefly the hardware and software
architecture related the social TV system (known as
ResNet.TV) we have developed and which has beenFigure 2: The main client viewing page developing a flexible context model that may be
The personalisation of content is achieved via filters applicable to a wide range of viewing contexts. Our
located on the right of the interface and allows users to model builds on our own previous work on applications
reorder the results by: such as the GUIDE context-aware tourist guide [3] and is
based upon Dix et al’s [4] taxonomy for context with
• Popularity: users may choose between channel order, respect to human-computer interaction of a system. This
channels that are popular at that moment, or broad taxonomy can be applied to encapsulate the
channels which have been popular that day. particular context types relevant to, and focussed on, in
• Social Awareness: allows users to restrict the usage our work, which are the who (user identity), when (date
data displayed to them which calculates popularity to and time), where (user location), what (activities and
relate to everyone or just their Facebook friends. history of activity) and how (access technology/device)
In addition to the core navigation and content retrieval and, uniquely, social (people in the virtual or physical
features, we additionally provide access to third party vicinity) and resource management (device or network
services such as YouTube, Twitter and Facebook. The resource usage or availability).
YouTube widget shows context-sensitive popular videos Context data pertaining to user identity and activity (who
based on the currently playing programme’s title. The and what) is derived implicitly by the system during
social panel offers either a contextual Twitter feed interaction with the system. In general, user profiles may
produced by querying using the currently playing be constructed manually (explicitly) by the user or
programme’s title, or a Facebook feed of which automatically (implicitly) by a system. We have chosen
programmes each friend is currently watching or to focus on implicit profile creation first based on each
programme they watched last. user’s content usage history since this provides the data
necessary to provide the required intelligence.
3.3 Monitoring Platform Additionally, from our previous work involving explicit
JavaScript is used extensively to instrument the user user profiling [3] we know that is often difficult to rely on
interface in order to gather activity information. Each user this model alone [4].
interface request generates an AJAX HTTP POST request
to the web server, which in turn is processed by a PHP 4 RESULTS
script, generating an entry in the central database hosted In our recent work [7,8] we have provided comprehensive
on the database server. The POST request contains results pertaining to the analysis of our initial trials
several parameters and some are sent with every request covering a 7 month period from October 2009 to May
(highlighted using italics), whereas other parameters are 2010. Throughout this period we have recorded over 4
specific to the type of event generated. million discrete events, corresponding to over 4,500
• General Events: Date/time of event, User IP address, unique users, over 65,00 browser sessions and
Browser session ID, Interface version (e.g. staff or approximately 20% of active Facebook accounts. In
student), Time between last channel change, Browser summary, during this period we have found that:
or tab focused/lost focus. • Significant correlations exist between individual user
• Media Playback Events: Channel currently being and the overall (i.e. global) popularity of programmes
watched, Channel launch / channel exit, play/pause, on offer and that these correlations could be
Full-sceen or window mode, volume up/down. leveraged in order to predict the potential popularity
• Navigation Based Events: Main navigation bar of a particular piece of content given a user’s content
selection, Channel order/by popularity filtering consumption history.
selection, Social filtering selection, Category selected. • To predict global popularity of a piece of content
• Social Interaction: YouTube video selected, Twitter some of the most telling measures we found involved
post hyperlink/profile hyperlink followed, Facebook a user’s historical average percentage watches (i.e.
login/logout, Facebook unique ID, Thumbs up/down. users whom tend to watch all of a given programme),
number of (Facebook) friends also using the service
Upon receiving the POST request, the PHP script and details relating to the way in which they search
calculates the position of the currently playing and navigate (in our system this relates to carousel
programme in the popular right now/my (Facebook) reconfigurations).
friends carousel, regardless of the actual carousel
configuration selected by the user. So, for users that • Almost 25% of active sessions include explicit
always watched the same programme as their Facebook Facebook logins while using the service. Although
friends, their average calculated ‘position in friends list’ this is not a particularly high figure at this early stage,
metric would be 1, representing that the programmes they we currently do not include any guidance on the
watch are always in the first position in the carousel. benefits of using the ‘Connect with Facebook’ feature
or describe how it works, what it does or what the
It is this instrumentation mechanism which provides us privacy implications are. Our landing page simply
with very detailed records of each user’s session. These states ‘Connect to your Facebook account to see what
records are kept for the purpose of presenting content your friends are currently watching.’ While this was
filtered by popularity as well as for historical analysis. an intentional decision so that we could measure the
For the purposes of our research, we are interested inwillingness of users to sign-up without being made aims to highlight some of these areas as well as highlight
aware of the implications, we were surprised by this some of the lessons we have learned.
high figure overall given the very strong references The overriding challenge we feel is worth keeping in
and concerns related to privacy expressed during our mind is ‘scale’ and especially ‘scale to the web’ in order
focus groups. that any approach be effective and/or adopted given the
• By comparing usage of our system between those nature of future TV services being based on a hybrid of
users that signed in with Facebook and those which traditional broadcast and more recent web based standards
chose not too, we found an interesting result. The and technologies but which, additionally, need to address
more often a ‘social user’ visits the service and the following.
experiences the content the more unique channels
they tend to experience. In contrast, those that 5.1 Findability
choose not to use the social features experience fewer Findability (not content) is King! Namely, the ability for
channels. While this requires further investigation viewers to efficiently and effortlessly discover new (and
we speculate that this is due to the social awareness existing) content. This simple process would benefit from
features built in to the interface which exposes what advances in multimedia search and discovery mechanisms
other friends are or have been watching and that this which can intelligently leverage content, content metadata
does impact choice. and user generated metadata, personalisation and accurate
• Difference were present between the number of recommendations. A goal could be to support retrieval of
different channels watched and how many of these content based on questions such as “Show me all the
were in the top 10 most popular each day. We found content from this week which my friends have watched
that the users which made use of the social awareness which I have not and which I am interested in”.
features was higher and that they watched 4/10 5.2 Visualisation
compared to 2/10 on average.
Search and discovery of new content requires adequate
• The average length of time the first channel selected
representation to the end-user. In systems which are
was viewed is increasing over time, which suggests
aggregating live, on-demand/catch-up and web content,
that users are choosing a specific channel
new approaches to the visualisation and presentation of
immediately rather than browsing (or channel
EPG data beyond the traditional ‘grid’ are required.
hopping) on arrival at the service.
Mechanisms for appropriately displaying and enabling
While the above provides a brief summary of our findings, navigation of content is, as has been discovered in the EU
they also pose many questions or lines of enquiry which funded P2P-Next project, constrained by the end
are open to investigation still. We believe there are system/device which may be a pc/laptop, mobile or low
lessons to be learned from studying of usage such as a powered CE device. The ability to re-use and/or re-
user’s actual viewing behaviour, their social connections purpose data and customise for individual end-user
and interactions, and their high level interactions with the context is a major challenge especially when one wishes
system as a whole (i.e. interface events). The following to offer the same or similar user experience.
section defines a series of discussion topics and areas for
future work and potential collaboration. 5.3 Data Aggregation
Data aggregation requires new methods for supporting the
exponential growth and reuse of new content, unified and
5 FUTURE CHALLENGES simple access to distributed multimedia content assets of
In this paper we have thus far summarised our approach diverse formats, efficient methods for extracting data
and high level results relating to the development of a from activity streams and the social graph (e.g. activity,
Social IPTV platform. Our ongoing and future work for status, likes, comments, ratings, etc) which exploit current
the development of the Social TV platform is based on standards and inform new standards in television content
the forwarding of two key areas: metadata enrichment.
• User Interface to TV systems: Namely the 5.4 Metadata
advancement of applications which enable the
personalisation and recommendation Use of linked data, professional and user generated
semantic metadata specification and encouragement
• Distribution mechanism and effective delivery participation of tagging and adding new metadata to better
of media given the heterogeneous nature of support filtering/personalisation and the avoidance of the
devices and communications networks and ‘cold start’ problem. Mechanisms for representing user
technologies data statistics, strength of user interest(s) and user
Through the design, development and initial evaluation of context(s) in machine-readable format, e.g., RDF, FOAF.
our system with the student population we have identified
a number of areas of future work or themes which we feel 5.5 Identity and Privacy
are still inadequately addressed by the network and Personal identity is core to any social based network or
electronic media (NEM) communities. This last section service. While identity and identity management are facts
of life for web based services, this is still a newphenomena for TV, especially the notion of logging on to temporal indexing into their content, then they tap into the
your TV. However, the merging of the web and TV so-called wisdom of the crowd to index (and implicitly
worlds inevitably means identity management will rate) interesting fragments of their content.
increasing becoming part of TV and especially IPTV
based offerings. This area of research and development 6 CONCLUSIONS
raises questions surrounding new identity services, This paper has summarised our recent work on the
services and/or federated access between services and development of an IPTV service which exploits the social
service providers, single sign-on. Further, clear and graph for personalisation. Through the initial evaluation
unambiguous controls of user identity management and of this system we have obtained some valuable lessons
profile managements are required. Specifically, and results which highlight the potential effectiveness of
unobtrusive methods for making clear the privacy exploiting user activity information for both end-user
implications of changes to profiles and user settings. (personalisation) and (intelligent) content distribution
Additional features to enable a user to maintain control of systems. Our paper has concluded by introducing a
their profile(s), virtual identities, content and contextual number of broad themes and a series of open questions of
metadata and mechanisms to ensure system wide privacy thoughts which act as our motivation for continued and
integrity which are complicated when aggregating data new research projects within the Network and Electronic
from various repositories is introduced? Major questions Media (NEM) domain.
are raised when one considers how one ensure you are not
unwittingly revealing sensitive information during the
aggregation and enhancement process? References
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