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
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
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
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
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
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
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
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
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: [email protected][email protected]
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”
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)
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
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
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
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
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