Danbri 2nd Review WP3 Challenges

8/6/2019 Danbri 2nd Review WP3 Challenges

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WP3

Challenges & Hybrid models

Dan Brickley, VUA

Pro-netics & BBC


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Overview

Challenges for TV in Social Web

theory and practice of our hybrid approach

3 Interconnected problems: Privacy, Sparsity and Heterogeneity

What we built (and why)

different kinds of recommender ways of integrating them

Plans and options for final developments


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Theory and Practice

89 05 2 9

00 88 8 6

23 97 9 8


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More likely ...

09 00 0 9

00 88 0 0

00 97 0 8


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77


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88


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TV preference data is very sparse!

Even for a single service (eg. Netflix), data is

overwhelmingly sparse

For NoTubes open systems, challengesmultiply:

often no global view, only per-user data

many ways of identifying the same content item

many ways of identifying the same user

never mind other entities (actors, directors, ...)


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Challenges: Sparsity, Fragmentation

Content identifiers (WP1)

Wikipedia/DBpedia URLs? Freebase?

RottenTomatoes.com, IMDB.com, broadcaster IDs

Social Web interoperability

Bobs on Facebook, Charlies on Twitter

negotiating access to non-public data (OAuth)

reconciling metadata models, rating models


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Fragmentation by site


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A hybrid approach to sparsity

Find patterns and paths in factual data

Collaborative filtering - from bulk rating data

Experiments with big data (e.g. Twitter

crawl)

Models for combining recommenders

Strategies for inferring sameAs links

...or grouping items together (by series,

brand)


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Challenge: Privacy

TV preferences are very personal data

Relevant standards (OAuth) are new

deployed widely in Social Web during NoTube slower adoption in TV and broadcast world

We can use OAuth to request permission to

read a users closed data (eg. FacebookLikes)

limits ability to find general trends across an

entire audience (except public data - twitter?)


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Diversity and Fragmentation

Diversity of the Web

reading lists: bookcrossing, librarything, amazon

music on last.fm, spotify, ... news sites, social networks, blogs ...

How to integrate while respecting privacy?

Good news:O

Auth deployment growing &social sites expose their recommendations

Bad news: user-by-user data makes large-

scale analysis of trends harder


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OAuth? RDFa?

OAuth lets sites negotiate access with users

e.g., Facebook knows lots of movies I like.

NoTube can use OAuth to ask me to sharethat data with TV services

RDFa data from movie pages (IMDB, Rotten

tomatoes) is consumed at Facebook This makes certain pages attractive as content

identifiers, a taste graph alongside social

graph


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RDFa in IMDB and

RottenTomatoes HTML

Aggregated by Facebook (and then, by us...


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What we built

Main WP3 work: beancounter and pattern

recommender

Aggregate, normalize and merge social Webactivity streams, then match against enriched

TV metadata to produce recommendations

We also have a Mahout-based collaborative

filtering recommender, with item to item

recommendations based on bulk ratings data


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LOD challenges

Linked Open Data for TV is new

datasets evolving, changing

qualityv

aries modelling styles vary

lumpy, uneven coverage

Pattern recommender finds paths

from items in user profile to new content

handles variation between Linked Data sources


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Content Pattern-based

Recommendations Paths in Linked Open Data

Diversity & Serendipity measures

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Participation Pattern

Person Xplayed role Y

in TV program Z

194,649lmdb:actor triples

53,180 lmdb:director triples

28,549 lmdb:writer triples

1,262

lmdb:film_story_contributortriples

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Influence Pattern

Person X influenced

byperson Y (direct)

Person X andY

influenced byperson

Z (in-direct)

6,562dbpedia:influencedtriples

11,776dbpedia:influencedBy

triples

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Analysis of Patterns in Dataset

Dataset (BBC EPG metadata): 12,777 (7,756 title enrichment) programmes

1260 (401 enriched) brands (unique titles)

35,227 (19,394 enriched) person names in metadata

9,315 (4,590 enriched) unique person names in metadata

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# items

recommendations 1266

- Individual brands 411

paths 17,001

- with linkedmdb:actor 15,257

- with linkedmdb:director 1155

- with linkedmdb:writer 569

- with linkedmdb:film_story_contributor 20

# items

recommendations 222

- Individual brands 100

paths

- influencedBy (all) 1202

- influencedBy (unique) 521


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Collaborative filtering

(item similarity measures from bulk ratings data)


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Hybrid models:

factual paths and statistical similarity

(and not to mention @wossy is on Twitter with 1 million followers...)


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Status

We can show a standards-based system that

integrates TV preference data from diverse Web

matches this with enriched TV metadata

finds graph patterns linking users to content

integrates with classic recommender approaches

builds on opensource (Cliopatria,Mahout)

supports real-time multi-screen exploration


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Plans and challenges

Richer integration between components

currently this occurs in the application; can we

exploit LOD patterns prior toMahout analysis?

Polish & packaging; more patterns and rules

Track and influence evolving standards (W3C)

Work-in-progress with big data analysis -

what kinds of TV links are shared by the kind

of people who follow @stephenfry on

Twitter?

Documents

Danbri 2nd Review WP3 Challenges