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Plans on “Latent Topic Model”

Plans on “Latent Topic Model”. High-Level Architecture. Users. Ads. User Encoding. User Encoding. User Clustering. Prediction. eCTR / FB Prediction. Existing Pipeline. Encoding Auto-encoder for dimension reduction Political affiliation clustering

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Plans on “Latent Topic Model”

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  1. Plans on “Latent Topic Model”

  2. High-Level Architecture Users Ads User Encoding UserEncoding UserClustering Prediction eCTR / FB Prediction

  3. Existing Pipeline • Encoding • Auto-encoder for dimension reduction • Political affiliation clustering • Output: Hive table (user id + low-dim representation) • eCTR prediction • Optional: user clustering stage

  4. Approaches to use encoding in eCTR prediction

  5. Social Networks

  6. Information on a social network • Social graph • Friendship networks • User-ads network ... • Text • News feed • Messages • Ads text … • Images • Album • Random posts • Ads figures … • Demographics • Age, occupation … eCTR • Very high-dimensional • Non-independent • Insufficient training data (this is • true even we use the whole web) • Hard to optimize and interpret

  7. Essentials of a good user-ads representation • Distilling all local attribute semantics • Social roles • Topical contents • Ideology/sentiment • Capture relational information • long range indirect influence • social environments and contexts • Capture dynamic trends • e.g., change of strength of interest • New/dying interests • Discriminative: • optimize against well-defined predictive task rather than vague intermediate goals such as clustering • Low dimensional and (perhaps) interpretable

  8. Example:

  9. Proposed Models … …

  10. Dynamic tomography • How to model dynamics in a simplex? Trajectory of an individual/stock in the "tomographic" space Project an individual/stock in network into a "tomographic" space

  11. Senate Network: role trajectories Ben Nelson (#75) is a right-wing Democrat (Nebraska), whose views are more consistent with the Republican party. Observe that as the 109th Congress proceeds into 2006, Nelson’s latent space vector includes more of role 3, corresponding to the main Republican voting clique. This coincides with Nelson’s re-election as the Senator from Nebraska in late 2006, during which a high proportion of Republicans voted for him. Jon Corzine’s seat (#28, Democrat, New Jersey) was taken over by Bob Menendez from t=5 onwards. Corzine was especially left-wing, so much that his views did not align with the majority of Democrats (t=1 to 4). Once Menendez took over, the latent space vector for senator #28 shifted towards role 4, corresponding to the main Democratic voting clique. Cluster legend

  12. Visualization

  13. Visualization

  14. Algorithm Details

  15. Data

  16. Learning System Given – a network of user/documents z z z z Perform E-step (Gibbs sampling)in parallel way. Get Sufficient Stats Repeat until convergence α, β, η, μ α, β, η, μ α, β, η, μ Perform M-step In parallel way Single Program

  17. Scalable implementation of baseline user text model (M1) Discriminative M1 M1 + network model  M2 M3 + history + time  M3 Parallel work on downstream utility eCTR prediction Visualization User/ads clustering Project Plans and Milestones

  18. CMU: First intern Keisuke will come in mid Oct , implementing M1 Second intern Qirong Hu will come in later Dec, implementing M2 and M3 FB: Rajat Raina Rong Yang System support Resources

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