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G M D

I P S I

Recommending TV programson the WebBetween content based retrievaland social filtering

Patrick BaudischGMD-IPSI

March 3rd 98

March 8th 98

Credits

Thanks for the award (its almost done...)

ContentsPart 1

About the project Requirements An evolving system Personalization

Part 2 Recommendation and cooperative

aspects

Feedback & Conclusions

About the project A cooperation between GMD-IPSI &

GMD: German national reserach institute for information technology

TV-TODAY German printed TV program guide They sell 1,400,000 copies per two weeks Where are printed guides going when digital

TV and video on demand emerge?

About the project

Goal: Help users in creating their personal TV schedule

None of the German Web-based TV program system gives more recommendation than their printed counterpart

Be more than a prototype, reach thousands of users

Learn German now!

Design criteriaTV programs vs. books and movies TV programs are a stream rather than a

database=> We do not have much time to collect data for recommendations

TV programs are experienced as having a lower value => Require only low user effort

Users have experiences and therefore expectations from printed TV program guides (e.g. TV-TODAY)=> Start with what users expect

Design criteria

System must be easy to learn (WWW) => Do what people expect

Be spectacular (TV-TODAY) => Do what people don´t expect

An evolving system

1

2

3

guestsmembers

FamiliarityBehave like printedTV program guides

RetrievalQuery/Browsing

Personalization & FilteringAdjust permanent settingsProfile, Push service

First time user interface (guest mode)

What’ on tonight

We ran user tests: 40% of first time users plan only for today

Press Start

1

Genre Visualization

Table cells color-coded

List items have colored field

Hue = Genre

e.g. {sports=green, movie=red, ...}

Recommendation Visualization

Color intensity = relevance the darker the more recommended less recommended programs fade to background

color

What means “recommended”? (later slide)

Retrieval: Adjust four parameters

Date interval

Time interval

Channels (predefined set)

Genre

Press Start

2

Genre hierarchy

A Genre is the set of programs that match a descriptor

Deeper genres are more specific

Guides users

Less universal than boolean search

Create account / login

To personalize users need an account

Store user data on server side

Use this data matching users making

recommendations

--

Member user interface

Personalization

Personalize three of four parameters favorite times favorite channels favorite genre (There are no

favorite dates)

3

Personalize times

Click yellow buttons into hour fields

Draw whole rectangles at once (Mac Paint)

Personalize channels

(Applet Demo)

Select the German regional stations that you can receive

Personalize genres

Check favorite genres

Use folder with favorite genres like bookmarks

Click “all favorite genres” to load all at once

Personal schedule (“grocery list”) Select programs, print it out, take it home

RecommendationHow the colors are generated?

Is Social filtering applicable?

Chez. P Wally’s Beef-O Veggio Pizza.H McD’s

Joe D A B DJohn A F D FBrad C D BSue C C CBen A A AEllen F A FEthan D A A

(Diagram by Joe Konstan)

Applicability of the Ringo approach Correlate users by the programs in the grocery

list?

“In/Not in” info from the grocery list is much less informative than 7 ratings scale=> results of correlating people is rather poor

We don´t have unlimited time, only one week.

The database is not stable User A just returned from a 2 week vacation User B is a newbee

Correlate on a standard set of items means extra effort (amazon recommendation center)

correlation?

Applicability of the Grouplens approachGroupLens: Press 1,2, ..,7 to rate and go

to the next article

Joseph A. Konstan says: These ratings require high cognitive costs

=> Rating effort might be too much

Four types of recommendation

A Recommendationsby TV-TODAY

“Size of the audience”

Personal genre profile

Opinion leaders

B

C

D

The editors of TV-TODAY provide ratings for all movies of the day (60 of 1000 programs)

Ratings , , ,

You agree or you don`t

Recommendations by TV-TODAYA

Size of the audience

Use programs in “Grocery list” as a recommendation for other users

We count how often a program occurs in users “grocery lists”

The more the better the rating

Works for all programs not only movies

Will lead your attention to events like“Tour de France”

Not personalized => might not fit your personal interests

B

Initialization of “Size of the audience” Everyday one day is added, one removed

When a program is inserted into the system it is not in anyone´s “grocery list”

=> Initialize ratings from the genre or series

Remove initialization during the week and replace with the real recommendations

“Grocery list” means: “I want to see that”. It does not mean “I like that” (how could I know before I`ve seen it) (and afterwards nobody cares)

Anyway: It works!

Personal genre profile

Describe favorite genres in more detail

Based on public recommendation, but users define offsets to adapt ratings to personal needs

Andrea´s personal TV interests She is interested in sports, especially in

basketball, where she does not want to miss a single program.

She wants to be up-to-date about current information without spending too much time on it.

Finally, for recreation, she wants to include some good action movies.

C

Profile

Form based Interface

Define how many programs of this genre to get

Define how personally important these are

Form based Interface

Define for all favorite genres

Initialization:Small is important(Law of Zipf)

Graphical user interface

Grey = cropped

Yellow = selected

Red = important

Drag boxes around

Box sizes reflect number of programs available per week

Evaluation: Number of subjects

No deformation deformation

No tutorial 5 5

tutorial 5 5

(We just got started, ...)

Form based interface:

Graphical Profile Editor:

10 subjects

Comparison of the two interfaces The graphical interface is much more difficult

to learn than the form-based interface

The graphical interface provides more utility and is easier to use than the form-based interface precision graphical overview

=> Provide a form-based interface for first-time users and a graphical interface for frequent users

Learnability: There seems to be a lack of methaphors (Where is Don Gentner?)

Opinion leaders

Allow more individual users to generate recommendation (not only TV-TODAYs editors)

Loren/Phoaks: Not everybody wants to give recommendation, but some do

Take “Grocery lists” of an individual user as your personal source for recommendation(instead of summing all up)

D

Opinion leaders

Opinion leaders are represented as a folder containing their “grocery list”

An opinion leader behaves exactly like a genre

Users can have their favorite opinion leaders

Who benefits...?

Being an opinion leader means no extra work

“Don´t you want to become an opinion leader?”

But: Opinion leaders loose part of their privacy

Let´s reward them for that: Give them program data one week in advance

=> that helps initializing “size of the audience” A free subscription to the printed guide Tell them that it is “cool” to be one

Survival of the fittest: If a new opinion leader applies drop the one with the fewest subscribers

... and who loses?

TV-TODAY editors can be opinion leaders

TV-TODAY didn´t like the idea too much :)

Evaluation of the overall system so farOur group + TV-TODAY people (about 20

users)

Beta test at GMD IPSI with about 30 users

User tests with 10 users for 40 minutes each

Feedback

Orientation is easy, but undo is missing

For some users the system is still too complex (opening folders, buttons to small for elder users)

People liked the „grocery list“That´s good for our recommendation system

Overall it is useful and easy to use

High fun-factor!

„When will you go online?“

Future work

Go online! April 98

Where else can we apply the described techniques: Usenet news, web pages, ...

Be more proactive: Push service, email notification of very important programs

Scott Robertson (digital libraries): Soft pushes

Future work: Cooperative stuff

We do have a “Find similar users” component(based on favorite genres and genre profiles)

Allow users to exchange their profiles

Become an opinion leader for individual users (friends, community)

Recommend genres and opinion leadersThis allows managing a greater number of them

Have specific opinion leaders One that just recommends action movies, ... Keep them inside the genre structure

Profile creation is NOT JUST iterative

Three paths lead to Profiles

1. Creation

2. Outerrefinement cycle

3. Innerrefinement cycle

Producers ofDocuments

Distributors ofDocuments

Distribution andRepresentation

DocumentSurrogates

RegularInformation Interest

Users/Groups withLong-term goals

Representation

Comparisonor Filtering

Modification

Use and/orEvaluation

RetrievedDocuments

Profiles

Conclusions

Traditionally a broadcast medium TV makers broadcast, viewers watch Editors write, readers read Interactive and collaborative concepts are new

here

The system contains a lot of functions, some of which are more complex Users have months to discover all these

functions Until then retrieval is just fine Many users will never push it that far That´s ok!

The END

What do you think?