#sdow2011 keynote: user modeling and personalization on twitter

DelftUniversity ofTechnology

User Modeling and Personalization on Twitter SDoW, ISWC, Bonn, Oct 23, 2011

Fabian AbelWeb Information Systems, TU Delft

2User Modeling and Personalization on Twitter

#papers that use Twitter datasets

time2006 2007 2008 2009 2010 2011 2012

3User Modeling and Personalization on Twitter

Perspectives on Twitter data

Grrrr…that is gr8http://bit.ly/47gt3

@bob

What are Bob’s personal interests? What are his current demands?...

4User Modeling and Personalization on Twitter

PersonalizedRecommendations

Personalized Search Adaptive Systems

What we do: Science and Engineering for the Personal Web

Social Web

Analysis and User Modeling

user/usage data

Semantic Enrichment, Linkage and Alignment

domains: news social media cultural heritage public data e-learning

5User Modeling and Personalization on Twitter

User Modeling Challenge

I want my personalized

news recommendatio

ns!Analysis and User Modeling

Semantic Enrichment, Linkage and Alignment

Personalized News Recommender

Profile

?

(How) can we infer a Twitter-based user profile that

supports a news recommender?

6User Modeling and Personalization on Twitter

User Modeling FrameworkBuilding Blocks for generating valuable user profiles

Geert-Jan Houben Ke TaoQi GaoFabian, Qi, Geert-Jan, Ke: Analyzing User Modeling on Twitter for Personalized News Recommendations. UMAP 2011

7User Modeling and Personalization on Twitter

User Modeling Building Blocks

Profile?concept weight

?

time

1. Which tweets of the user should be

analyzed?

Morning:Afternoon:Night:

1. Temporal

Constraints

June 27 July 4 July 11

(b) temporal patterns

weekendsstart end

(a) time period

8User Modeling and Personalization on Twitter

User Modeling Building Blocks

Profile?concept weight

2. Profile Type

Francesca Schiavone won French Open #fo2010 ?

Francesca Schiavone

FrenchOpen

Francesca Schiavone French Open entity-

based

SportT

T topic-based

2. What type of concepts should represent

“interests”?

# fo2010

#fo2010# hashtag-

based

1. Temporal

Constraints

timeJune 27 July 4 July 11

9User Modeling and Personalization on Twitter

User Modeling Building Blocks

Profile?concept weight

2. Profile Type

Francesca Schiavone won! http://bit.ly/2f4t7a

Francesca Schiavone

3. Further enrich the semantics of tweets?

1. Temporal

Constraints

3. Semantic

Enrichment

Francesca Schiavone

Francesca wins French Open

Thirty in women'stennis is primordially old, an age when agility and desire recedes as the …

French Open

Tennis

French OpenTennis

(b) further enrichment

(a) tweet-based

10User Modeling and Personalization on Twitter

User Modeling Building Blocks

Profile? concept weight

2. Profile Type

4. How to weight the concepts?

1. Temporal

Constraints

3. Semantic

Enrichment

Francesca SchiavoneFrench OpenTennis

4. Weighting Scheme

timeJune 27 July 4 July 11

?weight(Francesca Schiavone)

Concept frequency (TF)

4

weight(French Open)weight(Tennis)

36

TFxIDFTime-sensitive

11User Modeling and Personalization on Twitter

(a)hashtag-based(b)entity-based(c)topic-based

User Modeling Building Blocks

2. Profile Type

1. Temporal

Constraints

3. Semantic

Enrichment4.

Weighting Scheme

(a)time period(b)temporal patterns

(a)tweet-based(b)further enrichment

(a)concept frequency

12User Modeling and Personalization on Twitter

AnalysisHow do the user modeling building blocks impact the (temporal) characteristics of Twitter-based user profiles?

(a)hashtag-based(b)entity-based(c)topic-based

2. Profile Type

1. Temporal

Constraints

3. Semantic

Enrichment4.

Weighting Scheme

(a)time period(b)temporal patterns

(a)tweet-based(b)further enrichment

(a)concept frequency

13User Modeling and Personalization on Twitter

Dataset

timeNov 15 Dec 15 Jan 15

20,000 Twitter users

10,000,000 tweets

2 months

more than:

75,000 news articles

WikiLeaks founder, Julian Assange, under arrest in

London

Available online: http://wis.ewi.tudelft.nl/umap2011/

14User Modeling and Personalization on Twitter

Size of user profiles

entity-based

topic-based hashtag-based

~5% of the users do not make use of hashtags hashtag-based profiles are empty

Entity-based user modeling succeeds for 100% of the users

Profile Type

15User Modeling and Personalization on Twitter

Tweet-based

further enrichment(e.g. exploiting links)

topic-based user profiles

More distinct entities per profile

further enrichment(e.g. exploiting links)

Tweet-based

entity-based user profiles

Impact of Semantic Enrichment

Exploiting external resources allows for significantly richer user profiles (quantitatively)

More distinct topics per profile

Semantic Enrichment

16User Modeling and Personalization on Twitter

?

User Profiles change over time

d1-distance:

difference between current profile and

past profile

€

d1(r p x,

r p current ) = | px,i − pcurrent ,i |i

∑

Example:

€

d1(0.50.50

⎛

⎝

⎜ ⎜ ⎜

⎞

⎠

⎟ ⎟ ⎟,

0.50

0.5

⎛

⎝

⎜ ⎜ ⎜

⎞

⎠

⎟ ⎟ ⎟) =1

music

footballtennis

old new

Hashtag-based profiles change stronger than entity-based and topic-based profiles

#

TThe older the profile the more it differs from the current profile

Temporal Constraint

s

17User Modeling and Personalization on Twitter

Temporal patterns of user profiles

topic-based user profiles

weekday vs. weekend profilesd1(pweekday, pweekend)

day vs. night profilesd1(pday, pnight)

1. Weekend profiles differ significantly from weekday profiles

2. the difference is stronger than between day and night profiles

2

Temporal Constraint

s

18User Modeling and Personalization on Twitter

Observations• Semantic enrichment allows for richer user profiles• Profiles change over time: fresh profiles seem to

better reflect current user demands• Temporal patterns: weekend profiles differ

significantly form weekday profiles

19User Modeling and Personalization on Twitter

EvaluationHow do the user modeling building blocks impact the quality of Twitter-based profiles for personalized news recommendations?

(a)hashtag-based(b)entity-based(c)topic-based

2. Profile Type

1. Temporal

Constraints

3. Semantic

Enrichment4.

Weighting Scheme

(a)time period(b)temporal patterns

(a)tweet-based(b)further enrichment

(a)concept frequency

And can we benefit from the findings of the analysis to improve recommendations?

20User Modeling and Personalization on Twitter

Twitter-based Profiles for Personalization

• Task: Recommending news articles (= tweets with URLs pointing to news articles)

• Recommender algorithm: cosine similarity between user profile and tweets

• Ground truth: re-tweets of users• Candidate items: news article tweets posted

during evaluation period

time

P(u)= ?

1 week

Recommendations = ?

5.5 relevant tweets per user

5529 candidate news articles

21User Modeling and Personalization on Twitter

Overview: Performance of User Modeling strategies

Entity-based strategy improves the recommendation quality significantly (MRR & S@10)

Topic-based strategy improves S@10 significantly

T

#

Profile Type

22User Modeling and Personalization on Twitter

Impact of Semantic Enrichment

Tweet-based

Further enrichment

Further semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles!

T

Semantic Enrichment

23User Modeling and Personalization on Twitter

Impact of temporal characteristicsSelection of

temporal constraints depends on type of

user profile.

•Topic-based profiles: adapting to temporal context is beneficial• Entity-based profiles: long-term profiles perform better

Adapting to temporal context helps?

yes

no

yes

no

T

T

time

startcomplet

eend

complete: 2 months

Recommendations = ?

startfresh

fresh: 2 weeks

time

start end

Recommendations = ?

weekends

Temporal Constraint

s

24User Modeling and Personalization on Twitter

Observations• Best user modeling strategy: Entity-based > topic-

based > hashtag-based • Semantic enrichment improves recommendation

quality • Adapting to temporal context helps for topic-based

strategy

25User Modeling and Personalization on Twitter

3 Research QuestionsEngineering-UM-Personalization Perspective

26User Modeling and Personalization on Twitter

Semantic Web Engineering Perspective on Twitter (and other social) data

Social Webdata

Applications…that understand and

leverage Social Web data

Social Web vocabulary, e.g. Twitter language

Model of the application, e.g. news

categories

Mining Semantics

# fo2011

sports -> tennis

translate &

integrate

What is the actual impact of mining and integrating social data on the application?Evaluate!

27User Modeling and Personalization on Twitter

How can we find “information” in social (micro-)streams? How can personalization help?

Answer

Question

translate between query and Twitter vocabulary

compose answer

see also TREC Microblogging Task: http://trec.nist.gov/data/tweets/

1. Search on Twitter

28User Modeling and Personalization on Twitter

What kind of knowledge can we learn from users’ micro-blogging activities and how can we (re-)use it for what types of applications?

Applications…that understand and

leverage Social Web data

2. Re-using Twitter data in other applications

translate & integrate between application and Twitter vocabulary

29User Modeling and Personalization on Twitter

Example: improving product recommendations with Twitter data?

dbpedia:Dog

dbpedia:Food

dbpedia:Mark_Haddon

I would never eat dogs!

30User Modeling and Personalization on Twitter

Narcissus

3. Personalization and Serendipity

How can we balance between personalization and serendipity?

Profile Cross-system UM: get complete picture about a personReasoning: what type of

things could surprise and interest a person?

Cross UM dataset: f.abel@tudelft.nl

31User Modeling and Personalization on Twitter

Thank you!

Twitter: @fabianabel