Empirical analysis of social recommendation systems Review of paper by Ophir Gaathon Analysis of Social Information Networks COMS , 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 sent there was the line: P.S. Get your free at HotmailHotmail

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 (1 st ) 10% off next purchase 10% off on product 10% off next purchase 10% off on product Buy (1 st ) 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.

productscustomersRecommendat ions edgesbuy + get discount buy + no discount Book103,1612,863,9775,741,6112,097,80965,34417,769 DVD19,829805,2858,180,393962,341 17,232 58,189 Music393,598794,1481,443,847585,7387,8372,739 VHS26,131239,583280,270160, Full542,7193,943,08415,646,1213,153,67691,32279,164 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- 1:69 DVD-1:108 CD-1:136 VHS- 1:203

The most active person made ~84,000 recommendations !!! + purchased ~4,400 different items !!! Participation Level power-law distribution With long 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 p t the probability that at time step t the recommendation exceeds the threshold) At time t+1, the total number of people in the cascade, N t+1 = N t * (1+p t ) Subtracting from both sides, and dividing by N t, 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 bitsforward recommendatio ns Percent (%) Book65,39115, DVD16,4597, Music7,8431, VHS Total90,60225,

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