Tom Sullivan & Joel Spitalnik - Data Science @ IRIS.TV - LA Data Science meetup - Sep 2014

Transcript

1. LA Machine Learning Group September 15, 2014
2. About IRIS.TV Leading in-player video recommendation engine designed to create continuous streams of personalized video SaaS based solution for publishers and video technology companies looking to increase views per viewing session. IRIS.TV enables publishers to: –  connect to their audience –  increase video views –  control their programming to maximize reach and revenue potential of every viewer
3. How it Works BEFORE IRIS.TV User has to navigate back to menu or app to search for more content
4. How it Works Powered by Adaptive StreamTM, IRIS.TV •  Adaptive machine learning •  Programmatic video delivery •  Personalizing experiences for audiences while enforcing editorial and business rules for publishers •  Improving content discovery in a manner that facilitates audience growth and interaction •  Re-inventing the way users consume video
5. How it Works Viewers of IRIS.TV enabled video players WATCH MORE, WATCH LONGER, WATCH OFTEN AFTER IRIS.TV INTEGRATION Video publishers are able to engage users for longer and serve more ads
6. Speakers Tom Sullivan Chief Data Scientist Joel Spitalnik VP Engineering
7. Agenda Personalization Response Curve Definition of Terms Data Science Engineering Summary
8. Personalization-Value Relationship Personalization Value No Response = Unemployment Positive Correlation Something else
9. Relationship Between Personalization and Engagement Stable Loss of value due to “creepiness” and loss of serendipity Strict increase in value (not likely given constraints) Value Personalization
10. Definitions Asset: A good that may be represented in an online environment and consumed in computerized or physical form Examples: Video, News article, Image, Sound file, Coupon, Book Track: An ordered collection of co-presented assets Anchor Asset: The first asset presented in a track (via landing or organic search) Present Deliver Consume (in part or whole) Select
11. Definitions Experience All tracks that result from interactions with a distinct anchor asset Engagement A user session that begins with the first track and ends after the final asset is presented and/or consumed Learning is based on track/experience/engagement history Asset  A   (Anchor)   Asset  D   Asset  F   Asset  A   (Anchor)   Asset  G   Asset  I   Asset  B   (Anchor)   Asset  Q   Asset  T   Dynamic Update TRACK A1 TRACK A2 TRACK B1 EXPERIENCE EXPERIENCE ENGAGEMENT
12. Hybrid (front/back end) Personalization IRIS.TV Approach Value Personalization  P0                                P1                                              P2                                        P3              P4                  P5  
13. The Constrained Optimization Problem Provide personalized recommendations, P, that maximize the value of variable on the vertical axis of the response curve, V (value) Subject to constraints (including estimation risk), : Λ While remaining aware of residual benefits, R •  The data scientist's desire execute machine learning to improve accuracy of predictions (recommended assets) •  The opportunity to generate residual value based business insights That can be computed given the available resource environment and acceptable latency in updates to tracks, E max (V + R) = f(P) s.t. Λ,Ε “A sensible estimate is an interval estimate” IRIS.TV uses the “Strategy first” approach rather than a “data first” approach
14. Building Personalized Tracks For any given anchor asset, a pre-calculated track is based on an evaluation of pairwise asset similarities reflecting a linear combination of An asset-to-asset structural component: a weighted composite similarities among the k asset features s(1) A user behavior component based on historical interactions with presented tracks s(2) Dynamically adjusted in based on user interactions with current track
15. Track Construction is a Clustering Problem Note: similarities may not necessarily be symmetric when using user behavior data Asset j (in track) Present Consume Like: Dislike Skips Fully Consume Similarity S(2) 2 10,345 95.1% 7.2 : 1 11.6% 77.3% S(2) 1,2 7 4,235 9.1% 1 : 27.9 54.3% 15.9% S(2) 1,7 Sample of Historical Data Related to Paired Asset Engagements for Anchor Asset 1
16. Temporal Observation Windows Temporal user behavior data may perish over time In our machine learning “sandbox”, there is little need to store and process data that has “aged” beyond some threshold The “optimal” temporal window may be inferred by using subject matter expertise and/or by ML We only consider the sufficient statistics, aggregated from the full log data and updated. This reduces the amount of data in the current environment and may improve computational efficiency, E max (V + R) = f(P) s.t. Λ, Ε Age   Obs  weight   t*   1   0  
17. Preparing the data for similarity calculations API Aggregation Queries Summary observation data in temporal window, constraints, asset metadata Raw Log Data
18. The Data Scientist at IRIS.TV Skills and Experience •  Ability to program in R •  Understanding of MySQL •  Some familiarity with working with large, diverse data sets •  Can move data between Hive/MySQL/R Differentiator •  Primary Strength is Statistics v. Data Management •  Understands dimension reduction and the pros/cons of using various methods •  Choices are driven by objective function max (V + R) = f(P) s.t. Λ,Ε Join us?: www.iris.tv Email: tom@iris.tv joel@iris.tv
19. Feature 1: Keyword Overlap Feature 2: Asset Length Tracks are Sensitive to Choice of Methods Asset A (anchor) Asset B Asset C A B C D E Asset A (anchor) Asset B Asset E Asset A (anchor) Asset B Asset D Single-link 2NN Complete Complete method: •  leads to possibly fewer computations (when n is sufficiently large) •  Can result more spherical clusters with respect to anchor asset •  Increases “track strength”
20. Increasing Personalization: Asset-to-Asset Feature Weighting •  Which features are more relevant in the user’s decision to consume assets? •  Pairwise similarity, between two assets s(1) i,j are a weighted sum of k separate feature- level similarities from asset metadata (e.g. length, keyword overlap, genre, publication date, mood, etc.) •  We use simulated annealing, simplex-marching, tree-based starting points, parsimony penalty, and loss values driven by (E,R)
21. Increasing Personalization with Groups The user population may be partitioned into groups based on observable characteristics and revealed feature preferences, each having a distinct set of composite similarities – leading to possibility of different tracks for each group Personalization-driven user partitioning based on location (and possibly device type) to improve “cold start” Other  Fans   World Asia   Europe   S.  America   Not  Brazil   N.  America   USA   Not  USA   Red  Sox  Fans   Yankee  Fans   Bots   Brazil  
22. Bots and Bias •  Not all user engagement data is useful – there are non-human users whose influence we try to remove / downweight the user behavior component of similarity computation •  e.g. One user selected 1600 videos in 10 minutes •  In polarizing topics such as sports, politics, etc., and quality assessments there may be bias in the like /dislike actions Braz   USA   Red  Sox  Fans   Yankee  Fans   Other  Fans   Bots   X  
23. Bucket Re-Evaluation Bucket assignment is a fuzzy classification problem and we assign a user to the bucket with the highest membership probability Periodically, we revisit the buckets and memberships •  If every bucket has only one user, the backend has generated personalization in its highest form (with respect to the IRIS.TV definition) •  As more data becomes available on a user based on their historical engagements, their bucket assignment may change
24. Hybrid (front/back-end) Personalization, so far Value Personalization  P0                                    P1                                                              P2                                              P3                    P4                        P5   Anchoring +similarity   +  Historical  user   data   +  buckets   +beVer  buckets   +real-­‐Xme   feedback   Next  up:  Joel  Spitalnik,  to  talk   about  the  technology  stack,  NLP,   and  dynamic  track  adjustment    
25. Front End Origins of Recommendation System JukeboxTV Manual Curation Building Data Structures Data Munging Natural Language Processing Mapping Keywords Service Oriented Architecture Agile Development Minimizing Latency
26. Preparing the data for similarity calculations Online Rec System Aggregation Queries Summary observation data in temporal window, constraints, asset metadata Raw Log Data API
27. Thank You Questions? www.iris.tv tom@iris.tv joel@iris.tv rohan@iris.tv