1. --WWW 2010, Hongji Bao, Edward Y. Chang
AdHeat: An Influence-based Diffusion Model for Propagating Hints to Match Ads
--WWW 2010, Hongji Bao, Edward Y. Chang
Google Research, Beijing, China
Presented by Flame Wang

2. Outline Social Network Ad Model Algorithms Experiment Results
Relevance Model
AdHeat: Influence Model
Algorithms
Experiment Results
Conclusion

3. Ads Model: by Analyzing Relevance

4. Content-based Ad Model
Ads
Analyzing webpage content relevance
Not personalized

5. Social Network Ad Model
Ads
User-targeting
Targeting Ads at Social Network Users
Mining Profiles, Friends & Activities for Relevance

6. Social Network Ad Model
Activities
Profiles
Friends

7. Disadvantage for Social Network Ad Relevance Model
Observation:
Influential users do not click on relevant ads.

8. Disadvantage for Social Network Ad Relevance Model
Observation:
Non-influential user data are too sparse for relevance analysis.
Influential users attract and are followed by many non-influential users, like a heat source.

9. AdHeat Model Considering both relevance & influence
To solve data sparsity.
Propagating influence by heat diffusion
To improve ad matching relevance.

10. AdHeat Model Social Graph Construction Relevance Analysis
(Hint word generation)
Influential User Ranking
Influence Propagation

11. AdHeat Model
Influence

12. AdHeat Model Hint Word Generation
Construct a latent layer for better semantic matching in relevance,
Each user is presented by some hint words,
Latent Dirichlet Allocation (LDA).

13. Latent Dirichlet Allocation [D. Blei, M. Jordan 04]

14. Hint Word Generation

15. AdHeat Model Influential User Ranking Social graph construction
A weighted directed graph G(U,E), U: users, E: edges,
: weight of user i dependence to user j
Edges built by the frequency and quality of users’ interactions.
BBS/Forum: no. of post’s page view
Q&A: some features of answers

16. Q&A User
Features:
[Xiance Si,09]

17. Influential User Ranking
Influential users defined by level of activity & authority.
HITS algorithm to determine influence score
Hub score: activity to propagate information
Authority score: authority to provide contents

18. HITS HITS algorithm Iteratively update till convergence
Influence score:
W: adjacency matrix of social graph,
: W with its rows normalized to sum to one,
: reset probability

19. Influence Propagation
Illustrative Example

20. Propagating Hint Words

21. Heat Diffusion Model
Heat equation:
Heat diffusion on directed graph ,

22. Heat Diffusion ,

23. Influence Propagation Algorithm

24. Experiment Results Dataset Google Confucius (Q&A community),
Half a million registered users’ data in one month for conducting AdHeat,
5,000 active users for targeting ads,
Ads data from Google AdSense.

25. Evaluation
Metric:

26. Evaluation
Influence-based model without propagation and content-based model

27. Evaluation
Influence model with and without propagation

28. Conclusions AdHeat utilizes both relevance and influence.
Improve ad matching performance on CTR.
Solve the data sparsity problem.