Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing
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Editor's Notes
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)