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Differential Adaptive DiffusionUnderstanding Diversity and Learning Whom to Trust in Viral Marketing,[object Object],Hossam Sharara, William Rand, LiseGetoorICWSM, Jul 18th 2011,[object Object]
Introduction,[object Object],Viral marketing techniques rely on the social network among customers to spread product recommendations,[object Object],Key Assumption,[object Object],Peer-influence is both static, and independent of the product type,[object Object]
Motivation,[object Object],Ann,[object Object],Janet,[object Object],John,[object Object],Bob and Mary will definitely be interested. However, I think Ann is not much into movies,[object Object],Mary,[object Object],WOW… I’ll send it over to everyone,[object Object],Women’s fashion Store(Invite a friend and get 10% off your next purchase),[object Object],MovieRental.com(Refer a friend and get $10 off your next rental),[object Object],Bob,[object Object]
Motivation,[object Object],Peer-influence is dynamic,[object Object],Dependent on previous user interactions,[object Object],Viral marketing strategies have an implicit effect on the underlying social network,[object Object],	Sometimes changing the structure of	the underlying network altogether ,[object Object]
Motivation,[object Object],Peer-influence is dependent on the type of product being spread,[object Object],Users have varying preferences for different products,[object Object],Both factors play a role in product adoption,[object Object]
Background,[object Object],Popular Diffusion Models,[object Object],Threshold Models (e.g. Linear threshold model),[object Object],Cascade Models     (e.g. Independent cascade model),[object Object],Influence probabilities are assumed to be static, insensitive to the product type, and known in advance,[object Object]
Objectives,[object Object],Capture the diversity in user preferences for different products,[object Object],Model the change in influence probabilities across multiple campaigns,[object Object],Design a viral marketing strategy that takes these factors into account,[object Object]
Outline,[object Object],Case Study: Digg.com,[object Object],Differential Adaptive Diffusion Model,[object Object],Adaptive Viral Marketing,[object Object],Conclusion and Future Work,[object Object]
Case Study: Digg.com,[object Object],Social news website,[object Object],Users “submit” stories in differenttopics, which can then be “digged”by other users,[object Object],Users can “follow” other users to get their submissions and diggs on their homepage,[object Object]
Case Study: Digg.com,[object Object],Following links define the social network,[object Object],User submissions serve as  proxy of user preferences for different topics,[object Object],User diggs are analogous to product adoptions,[object Object]
Dataset,[object Object],Social Network (user-user following links),[object Object],11,942 users,[object Object],1.3M follow edges,[object Object],Digg Network (user-story digging links),[object Object],48,554 news stories,[object Object],1.9M digg edges,[object Object],6 months (Jul 2010 – Dec 2010),[object Object]
Observation 1User Submissions vs. Diggs,[object Object],Smaller percentage of users who digg stories in topics that vary significantly from the topics they post in ,[object Object],Most users adopt only stories of interest to them ,[object Object]
Characterizing User Submissions,[object Object],Focused UsersHighly skewed preferences toward one or two topics,[object Object],Biased UsersLess skewed preferences toward a larger set of topics,[object Object],Balanced (Casual) UsersAlmost uniform preference across all topics,[object Object]
Observation 2Effect of Homophily on Adoption,[object Object],Peers with different topic preferences lose confidence in each other’s recommendations over time (followed diggs/adoption     ),[object Object],Peers with similar topic preferences gain confidence in each other’s recommendations over time (followed diggs/adoption     ),[object Object]
Outline,[object Object],Case Study: Digg.com,[object Object],Differential Adaptive Diffusion Model,[object Object],Adaptive Viral Marketing,[object Object],Conclusion and Future Work,[object Object]
Differential Adaptive Diffusion,[object Object],The influence probability between two peers (u,v) for product category c can be re-written as,[object Object],Confidence of user vin u at campaign i,[object Object],Preference of user vin product type c,[object Object],[object Object],[object Object]
Experimental Evaluation,[object Object],Evaluate the model performance in predicting future adoptions,[object Object],We use the first four months in Digg.com dataset for learning the influence probabilities, and the last two months for testing ,[object Object]
Baselines,[object Object],We compare our approach with two baselines* that incorporate temporal dynamics in learning the influence probabilities,[object Object],Bernoulli,[object Object],Each product recommendation  Bernoulli Trial,[object Object],Influence probabilities are estimated using MLE over a given contagion time for each user,[object Object],Bernoulli-PC,[object Object],Same Bernoulli representation,[object Object],Partial credit for each recommending peer within the contagion period,[object Object],*A. Goval, F. Bonchi, and L. Lakshmanan. “Learning influence probabilities in social networks.” In Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM’10), 2010,[object Object]
Results,[object Object],The Adaptive model, taking both the  diffusion dynamics and the  users heterogeneity into  account, yields better performance,[object Object]
Outline,[object Object],Case Study: Digg.com,[object Object],Differential Adaptive Diffusion Model,[object Object],Adaptive Viral Marketing,[object Object],Conclusion and Future Work,[object Object]
Adaptive Viral Marketing,[object Object],User recommendations are most effective when recommended to the right subset of friends,[object Object],Highly selective behavior  Limited exposure,[object Object],Spamming  lower confidence levels, limited returns,[object Object],What is the appropriate mechanism for maximizing both the product spread and adoption? ,[object Object]
Adaptive Rewards,[object Object],Successful recommendations are awarded (αxr)units, while failed ones are penalized ((1-α) xr) units,[object Object],α  conservation parameter,[object Object],Most existing viral marketing strategies assume α=1 (no reason for the user to be selective),[object Object],The penalty term helps maintain the average overall confidence level  between different peers,[object Object]
Experimental Setup,[object Object],Agent-based models to simulate the behavior of customers in different settings,[object Object],When an agent adopts the product, it makes a probabilistic decision to send a recommendation based on its knowledge about the peers’ preferences,[object Object],The objective of each agent is to maximize its expected reward according to the existing strategy,[object Object]
Experimental Setup,[object Object],Two sets of experiments,[object Object],Fully observable: The agents are allowed to directly observe the preferences of their peers ,[object Object],Learning preferences: The agents have to learn the peer’s preferences based on their response to previous recommendations,[object Object],Simulate the diffusion of 500 campaigns for products from 5 different categories ,[object Object],We use a linear kernel for adjusting the confidence levels between peers after each campaign,[object Object]
Fully Observable,[object Object],Intermediate values for α (e.g. α= 0.5) consistently maintains high adoption rates and high overall confidence over large number of marketing campaigns,[object Object]
Learning Preferences,[object Object],Allowing agents to learn the preferences accounts for both the product preference as well as the confidence level,[object Object]
Effect of Spammers,[object Object],To test the robustness of our proposed method, we inserted spamming agents in the network,[object Object],A spamming agent forwards all product recommendation for all its peers, regardless of their preferences,[object Object],We set (α = 0.5)  for all the other agents, and vary the number of seeded spammers,[object Object]
Effect of Spammers,[object Object],The network adapts to the presence of spammers (dropping their confidence levels), and continues to maintain adoption levels through trusted links,[object Object]
Outline,[object Object],Case Study: Digg.com,[object Object],Differential Adaptive Diffusion Model,[object Object],Adaptive Viral Marketing,[object Object],Conclusion and Future Work,[object Object]
Conclusion,[object Object],Network dynamics and users heterogeneity have a considerable impact on user interactions,[object Object],The proposed adaptive diffusion model incorporates both aspects to better model the diffusion process,[object Object],Adaptive rewarding mechanism for viral marketing maintains higher confidence levels over time,[object Object]
Future Work,[object Object],Potential Applications,[object Object],Social Recommendation,[object Object],Collaborative Filtering,[object Object],Analyzing the impact of the proposed model on opinion leader identification,[object Object],Incorporating the time-variability aspect of user-product preferences in the model,[object Object]
Thank You,[object Object],Questions?,[object Object]
Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing

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Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing

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Editor's Notes

  1. With the increased customer resistance
  2. ----- Meeting Notes (7/18/11 15:48) -----encouraging her
  3. Add a slide for describing digg.com / add key words (submissions vs. diggs)
  4. One interpretation for the high divergence users that they are imitatorsWe use the topic distribution of the user postings as an influence-independent source for measuring preferencesFig1: KL-divergence between the topics of user submissions and diggsFig2: KL-divergence between user submissions (prefs) and uniform distribution of topics
  5. * Reduce text- In order to capture both the heterogeneity in user preferences as well as the temporal dynamics, we split the influence probability into two components- The product preferences are either given or can be inferred from an influence-independent source (such as user submission in the case of Digg)
  6. Mention that these baselines use the independent cascade model
  7. Color the series if possible We compared against two models that take the changing dynamics of the influence probability into account, but doesn’t address the user preference / heterogeneity aspect
  8. If a user is very selective and makes each recommendation to only a few friends, then the chances of success are slim due to limited network exposure. On the other hand, recommending a product to everyone may have limited returns as well, due to the effect of irrelevant recommendations on the confidence levels between peersThe natural question to ask now is
  9. Typical viral marketing strategies reward users for successful recommendation, but don’t penalize them for failed ones 0 representing fully conservative behavior and 1 representing fully nonconservative behavior.
  10. No text for alpha=1,0 – just arrowarrows for this and next
  11. In more realistic settings, the user friends learn them from her responses to different recommendation*The settings where users are allowed to learn preferences are better than the settings where users directly observe preference (prev slide) as this one
  12. Varying the percentage of spammers (individuals recommending any product to all their peers), and analyzing the over all effect (set alpha = 0.5)As percentage of spammers increases, the overall adoption and confidence level decreases. However, the network is able to adapt to their presence by assigning lower confidence levels to
  13. *Add future work / implicationsUtilized in social recommendations (incorporate both “trust” in a friend and their preference combined with your preference to make better automated recommendations – Facebook, x like this page)