Empirical analysis of socialrecommendation systems

Review of paper by Ophir Gaathon

Analysis of Social Information Networks

COMS 6998-2, Spring 2011, Topic #12: April 26th Columbia University

Focus

The dynamics of viral marketing

Jure Leskovec, Carnegie Mellon University

Lada Adamic, University of Michigan

Bernardo Huberman, HP Labs

ACM Transactions on the Web (2007)

Space

Leskove, Singh & Kleinberg. Patterns of influence in a recommendation network. Advances in Knowledge Discovery … (2006)

Viral Marketing:exploit the social interconnects

to gain product/service adaptation superlinearly

Hotmail

• $50K Ad budget

• Gain 18 million users in 12 months

• How ???

 At bottom of every email sent there was the line:

P.S. Get your free email at Hotmail

Claim 1: Mass marketing is not the best way to

attract people to your cause • Over saturation by ads

• $ Expensive $• Usually not very focused

Claim 2: Recommendations by people we know are more effective then input by unknown

individuals

• Our friends know what we like • Our friends and us are more likely to share interests and preferences

• We listen more to what our friends say (usually)• Recommendations can be intertwined in social interaction

• Inexpensive • Add value- user involvement

how do we buy electronics • 5 out of 10 do online research before buying

• 7 out of 10 ask friends and family for recommendations (Burke 2003)

Exploitation: have an incentivised personal recommendation

platform

Buy a product

Recommend

Buy (1st)

10% off next purchase

10% off on product

10% off next purchase

10% off on product

Buy (1st)

Recommend

The data set

• large online retailer (anonymous)• Data collected between June 2001 and May 2003

• ~4 million distinct customers • ~16 million recommendations• 550K products recommend• 99% of products are in 4 product groups:

– books– DVDs– music– VHS

Network is not “viral”

Service not spreading virally• growth of the customer base over time is surprisingly it was linear.

adding on average 165,000 new users each month. • Indication that the service itself was not spreading epidemically.• 94% of users who made their first recommendation without having

previously received one.• the largest connected component contains less than 2.5% (100,420)

of the nodes• BUT some sub-communities from better connected network

– 24% out of 18,000 users for westerns on DVD– 26% of 25,000 for classics on DVD– 19% of 47,000 for anime (Japanese animated film) on DVD

• While others are just as disconnected– 3% of 180,000 home and gardening– 2-7% for children’s and fitness DVDs

First aid study guide First Aid for the USMLE Step

Oh My Goddess!: Mara Strikes Back.

products customers Recommendations

edges buy + get

discount

buy + no discount

Book 103,161 2,863,977 5,741,611 2,097,809 65,344 17,769

DVD 19,829 805,285 8,180,393 962,341 17,232 58,189

Music 393,598 794,148 1,443,847 585,738 7,837 2,739

VHS 26,131 239,583 280,270 160,683 909 467

Full 542,719 3,943,084 15,646,121 3,153,676 91,322 79,164

DVD – 10 recommendations per user Books and CD – 2 recommendations per user VHS - ~1 recommendations per user

How infulational the recomdaitons are? (Hit rate) Books- 1:69DVD- 1:108CD- 1:136VHS- 1:203

100

105

100

102

104

106

108

Number of recommendations

Co

un

t= 3.4e6 x-2.30 R2=0.96

The most active person made ~84,000 recommendations !!! + purchased ~4,400 different items !!!

Participation Level

power-law distribution Withlong flat tail

Size distribution of cascades

Sharp drop

Not a lot of long cascade

model of propagating recommendations• Above a certain satisfaction threshold we recommend

• (Since exceeding this value is a probabilistic event, let’s call pt the probability that at time step t the recommendation exceeds the threshold)

• At time t+1, the total number of people in the cascade,

Nt+1 = Nt * (1+pt)

• Subtracting from both sides, and dividing by Nt, we have

model II

• Summing over long time periods

• The right hand side is a sum of random variables and hence normally distributed (central limit theorem) .

• Integrating both sides, we find that the number of recommendations, N, is log-normally distributed

if large resembles power-law

product category

buy bits forward recommendations

Percent (%)

Book 65,391 15,769 24.2

DVD 16,459 7,336 44.6

Music 7,843 1,824 23.3

VHS 909 250 27.6

Total 90,602 25,179 27.8

More is better?

“Yes! Now I want to buy”

• Retailers might hope to boost revenues through viral marketing• the additional purchases that resulted from recommendations are just a drop in the bucket of total sales.• interesting insights into how viral marketing works (that challenge common assumptions made in epidemic and rumor propagation modeling) • frequently assumed in epidemic models that individuals have equal probability of being infected every time they interact. • Observed here that the probability of infection decreases with repeated interactions • excessive incentives for customers to recommend products could backfire - weakening the credibility

Summery I

• there are limits to how influential high degree nodes are in the recommendation network

• more and more recommendations (past a certain number for a product)

success rate per recommendation declines. • characteristics of product reviews and effectiveness of recommendations vary by category and price• more successful recommendations being made on technical or religious books

• placed in the social context of a school, workplace or place of worship

•model shows that smaller and more tightly knit groups tend to be more conducive to viral marketing• purchases and recommendations follow a daily cycle•customers are most likely to purchase within a day of receiving a recommendation• acting on a recommendation at atypical times increases the likelihood of receiving a discount

Summery II

Thank You