TV Scout Lowering the entry barrier to personalized TV program recommendation

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

S3

T

user updates

system learnsT3

U3

bookmarkedqueries

user defines

system suggests

S2

U2

T2

queriesS1

U1

T1

start

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 provider

Movie databaseProgram descriptiondatabase

Query subsystems

Exact match filtering

DateTime Profile

ChannelProfile

feedback

QSA filtering

QSA profile

Retention tools

Vid

eola

bels

Laun

dry

list

Time DialogChannelDialog

Edi

tors

’tip

s

Use

rtip

s

Text

sear

ch Gen

res

Est

im.

Pop

.

AC

F

quer

yho

cad

• 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 How does

user like newscompared tosports…?

Cooperation with German TV TODAY17,000 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.