using social media data for online television recommendation services at rtÉ ireland
TRANSCRIPT
USING SOCIAL MEDIA DATA FOR ONLINE TELEVISION RECOMMENDATION
SERVICES AT RTÉ IRELAND
Unit for Information Mining and Retrieval (UIMR)
The Insight Centre for Data Analytics
National University of Ireland, Galway, Ireland
Andrea Barraza-Urbina, Hugo Hromic, Ioana Hulpus, Benjamin Heitmann, Conor Hayes, Neal Cantle
2nd Workshop on Recommendation Systems for
Television and Online Video – RecSysTV
September 19th, 2015
Vienna, Austria
Centre for Data AnalyticsAgenda
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Introduction
• RTÉ Use Case, Challenges, Research Goal
Our Approach
• Data Integration
• Proposed Solution Approach
Conclusion and Future Work
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Introduction
• RTÉ Use Case, Challenges, Research Goal
Our Approach
• Data Integration
• Proposed Solution Approach
Conclusion and Future Work
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Introduction
500 + watching hours
Live Broadcast
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Introduction
Recommendation Service
Lack of personal preference data such as ratings.
Lack of historicaluser session information.
Dynamic inventory and limited life span of recommendable
items.
Challenges:
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Introduction
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Introduction
Amazing acting this evening Rachel
#thevixenisback @RCPilkington
@RTEFairCity great script
Don't think I've ever laughed as
much, @damoandivor is hilarious
tonight, still trying to catch my breath
from all the laughing!
Really interesting RTÉ documentary
about the late Brian Lenihan. Didn't see it
advertised much. Worth a watch
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RTÉ Project
RTÉ Programme Preferences Implicit RTÉ Programme Preferences
Transfer Learning
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Introduction: Research Goal
Design a Recommender System that can offer
programme suggestions to an anonymous user,
based solely on information about the user’s current
session and inferred preferences of other RTÉ users
extracted from social media.
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Introduction
• RTÉ Use Case, Challenges, Research Goal
Our Approach
• Data Integration
• Proposed Solution Approach
Conclusion and Future Work
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Data Integration
USER TWEET
Fair City
EPISODE
MEDIA
LINKS
Amazing acting this evening Rachel
#thevixenisback @RCPilkington
@RTEFairCity great script
IMDb
DBpedia URI
PROGRAMME
SOCIAL MEDIA
RTÉ CONTENT
LINKED DATA
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Generating Recommendations
Collaborative Filtering Recommendation
User-Item Matrix
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Generating Recommendations
Collaborative Filtering Recommendation
Number of common users between all
pairs of programmes
Item-Item Matrix
Limitation: Data Sparsity
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mentions
reply
retweets
Community-based Recommendation
User – User Graph
w1
w2
w3
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Window time range:
From 2015-07-29 21:00:00
To 2015-07-30 20:59:59
# Users: 36648
# Tweets: 23232
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Community-based Recommendation
# Users: 34
# Tweets: 18
# Retweets: 30
[Laurenmx15]
"I wonder who's gonna move in the old
no 23 Slater household.. #eastenders"
[Tobiiiaaas]
"Ah psycho Abi is back #EastEnders"
"(On the plus side yay an episode
focusing on Dylan)! #Casualty"
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Generating Recommendations
Hybrid Recommendation
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Generating Recommendations
USER TWEET
Fair City
EPISODE
MEDIA
LINKS
Amazing acting this evening Rachel
#thevixenisback @RCPilkington
@RTEFairCity great script
IMDb
DBpedia URI
PROGRAMME
SOCIAL MEDIA
RTÉ CONTENT
LINKED DATA
Centre for Data AnalyticsAgenda
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Introduction
• RTÉ Use Case, Challenges, Research Goal
Our Approach
• Data Integration
• Proposed Solution Approach
Conclusion and Future Work
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Conclusion and Future Work
• RTÉ project presents a representative use case to explore the potential use
of microblogging data to enhance Recommendation Systems.
• We proposed novel recommendation approaches that learn the user-item
models from Twitter, and apply them to the context of an online TV Player.
• Evaluate our proposed solutions with user testing.
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