IN SEARCH OF RELEVANCE - Amazon Web Servicesaws-de-media.s3. munich, 11.10.2016 michael muckel in search

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  • Munich, 11.10.2016

    Michael Muckel

    IN SEARCH OF RELEVANCE

    MODERN RECOMMENDER SYSTEMS FOR MEDIA INDUSTRY

  • 2

    Glomex Overview

    Publishers

    Content providers

    Video Value Service

    Media Delivery Service

    Media Exchange Service

    Glomex

    External broadcasters

    Web-only content owners

    Non-P7S1 publishers

  • 3

    List Model for Recommendations

    1 Item

    2 Item

    3 Item

    4 Item

    N Item

    Top-N List

    Decreasing Relevance

  • 4

    Temporal Model for Recommendations

    1 Item

    Continuous Playlist

    2 Item 3 Item 4 Item ∞ Item

    Current Item

  • 5

    Relevance – Search Perspective

    Exploration

    Precision Se

    ar ch

  • 6

    Relevance – Recommender Perspective

    Exploration

    Precision Se

    ar ch

    Recommender

  • 7

    Two Sides of the Relevance Medal

    Search

    Recommender

  • 8

    Glomex Recommender - Customer Perspective

    Enable our Glomex Exchange Customers to efficiently select content with high potential for conversions

    Metrics: Playlist Add, Pick Top-N

  • 9

    Glomex Recommender – User Perspective

    Keep attention of users as long as possible

    Metrics: Video-View per Playlist, Video-Views per Session, Average View-Time

  • 10

    Anatomy of a Recommender System

    SCORE RANK FILTER

  • 11

    Anatomy of a Recommender System

    SCORE RANK FILTER

  • 12

    Popular Recommender Models - in a nutshell

  • 13

    SCORE - Content-Based Recommenders

    Tags:

    Publish Date:

    Title:

    Cast:

    Description:

    Brand:

    Hierachy:

    fun, joko, claas, comedy, pro7 ,circus halligalli, show, sport, interview, formula1, race of champions

    11.12.2015

    Ein unnötig kompliziertes Interview

    Joko Winterscheidt, Klaas Heufer-Umlauf, David Coulthard, Sebastian Vettel, Nico Hülkenberg

    Wir haben Joko endlich seinen Traum erfüllt, einmal in einem Formel 1-Wagen mitzufahren. Die einzige kleine Bedingung: Er muss währenddessen ein Interview führen.

    Prosieben

    Episode: 13 Series: 6

    Watch: http://bit.ly/2bO4Z3u

    http://bit.ly/2bO4Z3u

  • 14

    SCORE - Content-Based Recommenders – Search Problem

    Tags:

    Publish Date:

    Title:

    Cast:

    Description:

    Brand:

    Hierachy:

    fun, joko, claas, comedy, pro7 ,circus halligalli, show, sport, interview, formula1, race of champions

    11.12.2015

    Ein unnötig kompliziertes Interview

    Joko Winterscheidt, Klaas Heufer-Umlauf, David Coulthard, Sebastian Vettel, Nico Hülkenberg

    Wir haben Joko endlich seinen Traum erfüllt, einmal in einem Formel 1-Wagen mitzufahren. Die einzige kleine Bedingung: Er muss währenddessen ein Interview führen.

    Prosieben

    Episode: 13 Series: 6

    Find similar content based on descriptive metadata

  • 15

    SCORE - Context-Based Recommendation – Use Context

    Palina Rojinski

    Joko Winterscheidt

    Ina Müller

    Olli Schulz

    Web Mining with:

     Named Entity Recognition  Word Embeddings

  • 16

    SCORE - Collaborative Filtering

    User U 3 - 5 1 - -

    User U 1 0 1 1 0 0

    Explicit Feedback aka Ratings

    Implicit Feedback

    Circus Halligalli Dawn of the Gag 2

    Length of vector depends on number of items available

  • 17

    SCORE - Collaborative Filtering

    Item 1 Item 2 Item 3 Item 4 … Item N

    User 1 x x x

    User 2 x x

    User X x x ? x ? ?

    User-Rating Matrix

  • 18

    SCORE – User-User Collaborative Filtering

    Item 1 Item 2 Item 3 Item 4 … Item N

    User 1 x x x

    User 2 x x

    User X x x ? x ? ?

    Conceptual Model:

    Find users with similar preferences1

    Find and Score items from matching users that User X has not watched2

  • 19

    SCORE – Item-Item Collaborative Filtering

    Item 1 Item 2 Item 3 Item 4 … Item N

    User 1 x x x

    User 2 x x

    User X x x ? x ? ?

    Conceptual Model:

    Find items with similar usage patterns for item 31

    Find and Score items from matching users that User X has not watched2

  • 20

    SCORE - Collaborative Filtering with Matrix Factorization

    Item 1 Item 2 Item 3

    User 1 x x

    User 2 x

    User X x x ?

    ≅ ✕

    Conceptual Model:

    Decompose rating matrix into dense, low-rank matrices (e.g. Alternating Least Squares)1

    Use resulting “Taste-Space” for finding similar videos2

    Matrix U Matrix V

  • 21

    SCORE – Statistical Models

    Detecting (Hidden) Periodicities

    Statistical Trend Detection

    Possible Methods

  • 22

    Anatomy of a Recommender System

    SCORE RANK FILTER

  • 23

    RANK - Basic Ranking - Sorting

    Model e.g. Collaborative

    Filtering

    1 Item

    2 Item

    3 Item

    N Item

    Sort by Relevance Score

  • 24

    Which model should we then choose?

  • 25

    RANK - Hybrid Recommenders – Static Ensembles

    f(xi,wi)

    Score: xi Weight: wi

    1 Item

    2 Item

    3 Item

    N Item

    Model 1 Collaborative

    Filtering

    Model 2 Collaborative

    Filtering

    Model 3 Content-Based

    Filtering

    Model 4 Trending

    Genre

    Model N New

    Content

    Sort by Adjusted Relevance Score

  • 26

    RANK - Hybrid Recommenders – Learning to Rank

    Model 1 Collaborative

    Filtering

    Model 2 Collaborative

    Filtering

    Model 3 Content-Based

    Filtering

    Model 4 Trending

    Genre

    Model N Most-Watched

    Content

    f(xi,wi)

    Score: xi

    1 Item

    2 Item

    3 Item

    N Item

    Ranking Model

    Weight: wi User Feedback: Clicks User Context: Location, DeviceSort by Adjusted Relevance Score

  • 27

    Anatomy of a Recommender System

    SCORE RANK FILTER

  • 28

    FILTER – Basic Filtering

    1 Content

    2 Content

    3 Content

    N Content

    1 Content

    4 Content

    6 Content

    M Content

    Ranked List By Relevance Score

    Filtered List

    2 Content

    3 Content

    5 Content

    X

    X

    X

    Filter Logic

    Example Filter: Geo-Location

  • 29

    FILTER – Multi-Dimensional Filters

    1 Content

    2 Content

    3 Content

    N Content

    1 Content

    4 Content

    Ranked List By Relevance Score

    Filtered List

    2 Content

    3 Content

    5 Content

    X

    X

    XFilter Logic

    Real-world Filters

     Geo-Location  Device (type)  Age-Restriction

     Brands  Genres  ….

    5 Content

    5 Content

    5 Content

    X

    X

    X

    !

  • 30

    Alternate Approaches

    SCORE RANK FILTER

    Attribute X Examples: Genre, Rights (Geo-Restriction )

    Integrate into Models Integrate into Filter?

  • 31

    Glomex on AWS

  • 32

    Glomex Data Service

    Video Value Service Media Delivery Service Media Exchange Service

    Data Service

    Real-time-Monitoring Analytics Machine Learning

  • 33

    Glomex Data Service

  • 34

    Thank you for your attention!