designing a better online music store (by:larm 2008)

Designing a Better Online Music Store

by:Larm 2008

Vegard Sandvold

● Enterprise Search Consultant– Comperio AS (FAST

Search & Transfer)

● Music Technologist & Entreprenour– Musikkteknologen.no

– LiveRevolution.net

Outline

1. Why we need better online music stores

2. Power of The Long Tail

3. Role of recommender systems in e-commerce

4. Expert, social and content-based recommendations

5. Demo

6. Additional thoughts and conclusion

1.0

ShelvesClerk

http://flickr.com/photos/lynt/162883105/

1.5

Shelves

Clerk

Better... how?

● Rights Holders, Publishers and Retailers– «Make more money»

● Artists– «Visibility and promotion»

● Consumers– «Broaden my horizon»

– «Something new that will impress my friends»

– «I'm in the mood for some soft rock ballads»

Long Tail Economics

● The cost of shelf space online is ZERO● Therefore:

1. Make everything available

2. Help me find it

● «Recommender systems expose consumers to a larger selection of interesting and relevant music»

The Shape of The Long Tail

The Long Tail

The Hits

Items

Pop

ular

ity

Said in Another Way...

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Products

Sal

es (

$)

1

10

100

1 10 100

Products

Sal

es (

$)

Source: «I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System»,Cha et.al., ACM Internet Measurement Conference 2007.

What is a Recommender

● System that connects (relevant) items to items, items to users, and users to users

● Way to navigate large data collections● Content relevance filter● Important characteristics include:

– Transparency

– Familiarity vs. novelty

– Completeness

Orders of Information Management

1st order

Structuring

3rd order

Tagging and other

metadata

2nd order

Classification

Recommendation Strategies

1. Expert

2. Social

3. Content-based

Expert Recommendations

● «I'm telling you that you will like this, because I know a lot about music»

● Pros– Transparency of the recommendations

– Can differentiate between “good and bad” music, according to the expert

● Cons– Not personalized

– Limited coverage

– No scalingSource: Celma & Lamere, Music Recommendation Tutorial, ISMIR 2007

http://www.viruscomix.com/page398.html

Non-expert Recommendations

Social recommenders

● «You will like this, because it's popular with people like you»

● Pros– Works for and between everything

● Cons– Lack of transparency

– Already popular items stay popular (the rich get richer effect)

– Cold start, new items enter at the bottom

Source: Celma & Lamere, Music Recommendation Tutorial, ISMIR 2007

Artists Similar to U2

Small-world Networks

The Long Tail Reach of Amazon

Social Tagging

Pros● Order emerges from chaos

(folksonomies)● Layers of metadata

Cons● Polysemy, synonyms, spelling● Idiosyncracity● Sparsity

Content-based Recommendations

● «You will like this, because it sounds like something you already like»

● Objective musical similarity– Timbre, instrumentation, rhythm, tempo, intensity

● Pros– No popularity bias

– No cold-start

– No manual effort required

● Cons– Not so transparent

– Can't tell «good» from «bad»

Demo

Comperio Music Search

The Effect of CB Recommendations

Source: Celma & Lamere, Music Recommendation Tutorial, ISMIR 2007

User Ratings – Yes and No

● Very effective, but highly suggestive– We trust other people

– We tend to like what others like

● Can counteract Long Tail effects● This is viral marketing!

Viral Marketing

1. Social links and sharing

2. Widgets

Conclusion

● A Better Online Music Store is built on search and recommendation

● More money for Rights Holders, Publishers and Retailers

● Visibility and promotion for Artists● More music and fun for Consumers

Thank you!

● Check out «Widgets, Viral Marketing and Findability» by Andrew Dubber– 14:15 in this auditorium

Vegard Sandvold+47 48 23 92 32

vsandvold@gmail.com