TV ScoutLowering the entry barrier topersonalized TV program recommendation
Patrick Baudisch &Lars Brueckner
AH 2002June 1th 2002
Contents
• Motivation
• TV Scout user interface– Retrieval part…– …leading to the filtering part
• Results of usage data analysis
• Conclusions
Motivation: Information overload
• Too many research papers, books, movies, web pages… even TV programs
• Germany: printed program guides list 10.000 programs per two weeks
• Content of interest has not increased proportionally planning TV has become a challenge*
• Goal: Reduce the set of programs that users have to look at to find relevant programs Allow users to watch TV more selectively
The initial concept…
• We wanted to offer:personalized TV program listings“at a single mouse click”
• Resulting user interaction:“Sure, we’ll tell you what’s on tonight, but before we do that, please answer these 30 questions…”
• Guess how users liked that…
We did some field work…
• Users’ expectations are inspired by printed TV program guides– Step 1: Find the right listing– Step 2: Sift through the listing– Step 3: Remember or mark-up programs to watch– Step 4: Watch
User interface design challenge:– Pick people up where they are (printed TV program guides)– …– …and guide them to personalized listings at a mouse click
Best match
Step 1: Select a query
Exact match
programdescriptionlist
programdescription table
retentionmenus
Step 2+3: Read & retain program descript.
programdescriptionlist
programdescription table
retentionmenus
Step 4: Print it out & watch TV
video labels
laundry list
Emulating a printed guide
Printed program guide
Step 1: Pick the right listing
Step 2: Sift through listing
Step 3: Mark-up programs
Step 4: Watch
TV Scout
Step 1: Pick the right query
Step 2: Sift through listing
Step 3: Retain programs
Step 3b: Print it out
Step 4: Watch
But then: suggestions and bookmarks
Personalized schedules at a mouse click
Not that users have to, but…
Summary of usage
system compiles
one-clickTV program
one-clickTV program
S3
T
user updates
system learnsT3
U3
bookmarkedqueries
bookmarkedqueries
user defines
system suggests
S2
U2
T2
queriesqueriesS1
U1
T1
startstart
system provides
user writes
TV Scout usage data
• TV Scout user interface concept= delayed disclosure of the filtering functionality
• Does this actually reduce the entry barrier to personalized filtering?
• => Informal analysis of log file data from actual web usage
Procedure
• 18 months of log file data, extracted from the web server log files and the system’s database
• Gathered data– 10,676 registered users– In total, users had executed 48,956 queries– 53% of all queries (25,736 queries) were specific queries
different from the default query.
• Bias: the suggestion feature became available later
Goals
• Goal 1: Repeated usage would indicate that users had taken the entry hurdle
• Goal 2: Learn more about the users’ demand for the offered filtering functionality: How many would use bookmarking and/or query profiles?
• Goal 3: How useful users would find the query profile. Query profile users, would they use or abandon it?
Results & conclusions
• Repeated log-ins:9,190 of 10,676 users logged in repeatedly (= 86%)
• Very high percentage for a web-based system
• => Delayed disclosure of filtering functionality is a successful approach to keeping the entry barrier for first-time users low
Results & conclusions
• Bookmarks & Query profiles– 1770 users had bookmarked 4383 queries (= 17%)
– 270 users executed query profile (= 15% of bookmark users)
– They executed their query profiles 5851 times (21 times per user).
– Once they used the profile they liked it
• Only 17% used filtering functionality, isn’t that low?– Survey: only 12% of the users of printed TV guides planned TV
schedule for a week or longer
– => The 83% non-bookmark users may have found retrieval to be the appropriate support for their information seeking strategy
• Future work: An online survey as well as an experimental study should help to verify this interpretation.
Thanks to: Dieter Böcker, Joe Konstan, Marcus Frühwein, Michael Brückner, Gerrit Voss, Andreas Brügelmann, Claudia Perlich, Tom Stölting, Diane Kelly, and TV TODAY
Further reading & demo: http://www.patrickbaudisch.com
END
• If time left–Explain system architecture
–Demo paintable interfaces
TV ScoutArchitecture
program descriptions
Content providerContent provider
Movie databaseProgram descriptiondatabase
Query subsystemsQuery subsystems
Exact match filteringExact match filtering
Date
Time Profile
ChannelProfile
feedback
QSA filtering
QSA profile
Retention toolsRetention tools
Vid
eo
lab
els
La
un
dry
list
Time Dialog
ChannelDialog
Ed
itors
’tip
s
Use
rtip
s
Te
xtse
arc
h
Ge
nre
s
Est
im.
Po
p.
AC
F
quer
yh
oc
ad
• Slides to bring up during questions
TV Scout UI
TV Scout interface with starting page
viewing timeprofile editor
channelprofileeditor
querymenus
QSAmenu
textsearch
programdescriptionlist
programdescription table
suggest queries
QSAprofileeditor
QSA profileeditor (experts)
retentionmenus
video labels
laundry list
Structure of TV Scout user profiles
userprofile
QSAprofile
q1
A
qn…
e.g. news,sports,Comedyshows
e.g. news,sports,Comedyshows How does
user like newscompared tosports…?
How doesuser like newscompared tosports…?
Cooperation with German TV TODAY
17,000 registered users
TV Scout: retrieval usage summary
retention tools
TV listing& table
Further reading
• P. Baudisch. Dynamic Information Filtering. Ph.D. Thesis. GMD Research Series 2001, No. 16. GMD Forschungszentrum Informationstechnik GmbH, Sankt Augustin. ISSN 1435-2699, ISBN 3-88457-399-3.
• P. Baudisch. Recommending TV Programs on the Web: how far can we get at zero user effort? In Recommender Systems, Papers from the 1998 Workshop, Technical Report WS-98-08, pages 16-18, Madison, WI. Menlo Park, CA: AAAI Press, 1998.
• P. Baudisch. The Profile Editor: designing a direct manipulative tool for assembling profiles. In Proceedings of Fifth DELOS Workshop on Filtering and Collaborative Filtering, pages 11-17, Budapest, November 1997. ERCIM Report ERCIM-98-W001.
• P. Baudisch. Using a painting metaphor to rate large numbers of objects. In Ergonomics and User Interfaces, Proceeding of the HCI '99 Conference, pages 266-270, Munich, Germany, August 1999. Mahwah: NJ: Erlbaum, 1999.
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