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

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






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  • With the increased customer resistance
  • ----- Meeting Notes (7/18/11 15:48) -----encouraging her
  • Add a slide for describing digg.com / add key words (submissions vs. diggs)
  • 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
  • * 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)
  • Mention that these baselines use the independent cascade model
  • 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
  • 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
  • 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.
  • No text for alpha=1,0 – just arrowarrows for this and next
  • 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
  • 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
  • *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)

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

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