NoTube: Pattern-based Recommendations (part 3)

WP 3User profiling &

Recommenda6on (Part 3)BBC, Pro-‐ne+cs, VUA

1

Wednesday, March 28, 12

Contents

26-27 March 2012 2NoTube 3rd Review

Overview

User profilingGeneral goal & approachFrom activity streams to profileIssuesAnalyticsBeancounter

RecommendationsGeneral goal & approachSemantic recommendationStatistical recommendationHybrid recommendation

Exploitation

Conclusions


Overview

26-27 March 2012 3

TV Program Enrichment

SemanticPattern-based

Recommendation Strategy

RDF GraphTV

Programs

Semantic ContentPatterns for

TV Programs

HybridRecommendation

Strategy

StatisticalSimilarity-based

Recommendation StrategyUser Ratings &

Demographics(BBC EPG

Data)

EPG Metadata(BBC)

Recommendation Service

SimilarityClusters

of Programs

User Data Analysis

End-UsersEnd Users

NoTube 3rd Review


Overview

26-27 March 2012 3

TV Program Enrichment

SemanticPattern-based

Recommendation Strategy

RDF GraphTV

Programs

Semantic ContentPatterns for

TV Programs

HybridRecommendation

Strategy

StatisticalSimilarity-based

Recommendation StrategyUser Ratings &

Demographics(BBC EPG

Data)

EPG Metadata(BBC)

Recommendation Service

SimilarityClusters

of Programs

User Data Analysis

End-UsersEnd Users

BEANCOUNTER

NoTube 3rd Review


Statistical recommendations


• We had privileged access to two bulk user ratings datasets from BBC

• From these, used Apache Mahout toolkit to derive "item to item" similarity measures between each pair of items

• With larger (20k users) this worked well; with a smaller (1k) dataset, less well

• With BBC, investigating publication of these behaviour-derived similarity measures


31

Hybrid models:

factual paths and statistical similarity

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


Statistical recommendation


89 05 2 9

00 88 8 6

23 97 9 8

20k

12k


Statistical recommendation


09 00 0 9

00 88 0 0

00 97 0 8


99


1010


1111


1212


TV Preference Data is very sparse


• Even for a single service (e.g. Netflix), data is ‘overwhelmingly sparse’

• For NoTube’s open systems, challenges multiply:– 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, ...)

• Q: Can we tell a story about how organizations with such privileged overviews can contribute in a privacy respecting way to the public commons of linked data? (A: yes! see WP4)


Fragmentation by site



29


30


Statistical recommendation: Process


• Build on best-in-class opensource code, rather than re-invent

• Big-data ready (Hadoop-based)

• Of various options, LogLikelihoodSimilarity generally gave best results (standard 'withold some ratings' evaluation strategy)

• Other explorations: including large scale (1/2 billion tweet) Twitter analysis, Spectral Clustering, using demographics, ...


Exploitation & Further Development


Beancounter: •Pronetics’ user profiling SaaS•integration in the e-commerce technological solution

• making it more general purpose• making it capable of big data management a SaaS playground for Semantic Web researcher

•open source licensing•community extensions


Exploitation & Further Development


Recommendations: •explore further the combination of demographic stereotypes & semantics in a hybrid approach to learn a prediction model for the shows a user is most likely interested in•integrate in personalized semantic search frameworks•extend with additional LOD sources•test further the measures for diversity, serendipity and predictability

•open source licensing•community extensions


Acknowledgements



Technology

NoTube: Pattern-based Recommendations (part 3)