Using Social Media Data for Online Television Recommendation Services at RTÉ Ireland

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

2

Introduction

• RTÉ Use Case, Challenges, Research Goal

Our Approach

• Data Integration

• Proposed Solution Approach

Conclusion and Future Work


3

Introduction


Our Approach




Centre for Data Analytics

4

Introduction

500 + watching hours

Live Broadcast


5

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:


6

Introduction


7

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


8

RTÉ Project

RTÉ Programme Preferences Implicit RTÉ Programme Preferences

Transfer Learning


9

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.


10

Introduction


Our Approach





11

Data Integration

USER TWEET

Fair City

EPISODE

MEDIA

LINKS




IMDb

DBpedia URI

PROGRAMME

SOCIAL MEDIA

RTÉ CONTENT

LINKED DATA


12

Generating Recommendations

Collaborative Filtering Recommendation

User-Item Matrix


13


Collaborative Filtering Recommendation

Number of common users between all

pairs of programmes

Item-Item Matrix

Limitation: Data Sparsity


14

mentions

reply

retweets

Community-based Recommendation

User – User Graph

w1

w2

w3


15

Window time range:

From 2015-07-29 21:00:00

To 2015-07-30 20:59:59

# Users: 36648

# Tweets: 23232


16

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"


17


Hybrid Recommendation


18


USER TWEET

Fair City

EPISODE

MEDIA

LINKS




IMDb

DBpedia URI

PROGRAMME

SOCIAL MEDIA

RTÉ CONTENT

LINKED DATA


19

Introduction


Our Approach





20


• 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.

21

Technology

Using Social Media Data for Online Television Recommendation Services at RTÉ Ireland