interweaving trend and user modeling for personalized news recommendation

Delft University of Technology

Interweaving Trend and User Modeling for Personalized News Recommendation WI-IAT 2011 Lyon, France August, 2011

Qi Gao, Fabian Abel, Geert-Jan Houben, Ke Tao {q.gao, f.abel, g.j.p.m.houben, k.tao}@tudelft.nl

Web Information Systems Delft University of Technology

the Netherlands

2 Interweaving Trend and User Modeling

Personalized Recommendations

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


Research Challenge

Analysis and User Modeling

Semantic Enrichment, Linkage and Alignment

Personalized News Recommender

Profile

? time

Nov 15 Nov 30 Dec 15 Dec 30

trends

Influence?

(How) can we construct Twitter-based profiles to support news recommenders?

(How) do trends influence personalized news recommendations?

interested in:

people politics


Twitter-based Trend and User Modeling Framework

Twitter posts

current tweets

of Twitter

community

news recommender ?

Profile Semantic

Enrichment

Profile Type

Aggregation

Weighting Scheme

trends

time

user’s interests


Trend and User Modeling Framework

Profile? concept weight

Profile Type

Interpol looking for this person http://bit.ly/pGnwkK ?

Interpol

Interpol entity-based

Politics T

T topic-based

1. What type of concepts should represent “interests”?

time

June 27 July 4 July 11



Profile? concept weight

Profile Type

Interpol looking for this person http://bit.ly/pGnwkK

Interpol

2. Further enrich the semantics of tweets?

Semantic Enrichment

Interpol

wikileaks

Julian Assange

(b) linkage enrichment

(a) tweet-based

wikileaks

Julian Assange

WikiLeaks founder Julian Assange on Interpol most wanted list

WikiLeaks Julian Assange

http://bit.ly/pGnwkK



3. How to weight the concepts?

time

Nov 30 Dec 15 Dec 30

weight(Interpol)

weight(wikileaks)

weight(Julian Assange)

Semantic Enrichment

Profile Type

Weighting Scheme

TF

Nov 15



3. How to weight the concepts?

time


Semantic Enrichment

Profile Type

Weighting Scheme

TF

TF*IDF

Nov 15

weight(interpol) > weight(united states)

Time Sensitive

- Time sensitive weighting functions: smoothing the weights with standard deviation

σ(interpol) σ(united states) <


3. How does the weighting scheme impact trend profiles?

!"

#!!"

$!!"

%!!"

&!!"

'!!!"

'#!!"

'$!!"

'%!!"

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'$(''(#!'!"

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#!(''(#!'!"

##(''(#!'!"

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#%(''(#!'!"

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)!(''(#!'!"

!#('#(#!'!"

!$('#(#!'!"

!%('#(#!'!"

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'!('#(#!'!"

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)!('#(#!'!"

!'(!'(#!''"

!"#$%&'()

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4.5678,91+":1;-<"

=.28,.">,.82.+"

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#!(''(#!'!"

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#&(''(#!'!"

)!(''(#!'!"

!#('#(#!'!"

!$('#(#!'!"

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'!('#(#!'!"

'#('#(#!'!"

'$('#(#!'!"

'%('#(#!'!"

'&('#(#!'!"

#!('#(#!'!"

##('#(#!'!"

#$('#(#!'!"

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#&('#(#!'!"

)!('#(#!'!"

!'(!'(#!''"

!"#$%&'()

"$*&+!

,&-.%&

#/!&

*+,-.+"/.+-,+0"

12324"

503+467-"

The trending entities within one week (TF)

emphasize the emerging popular entities (time

sensitive TF*IDF)

Obituary: Leslie Nielsen

WikiLeaks founder on Interpol most

wanted list

Tiny Qatar will host the

World Cup

Weighting Scheme



time


Semantic Enrichment

Profile Type

Weighting Scheme

Nov 15

4. How to combine trend and user profiles?

Aggregation Trend Profile

User Profile long term user history

current trends

d*

(1-d)*

aggregated profile


Experiment: News Recommendation •  Task: Recommending news articles (= tweets with URLs pointing to news

articles)

• Dataset: > 2month; >10m tweets; > 20k users

•  Recommender algorithm: cosine similarity between profile and candidate item

•  Ground truth: (re-)tweets of users (577 users)

•  Candidate items: news-related tweets posted during evaluation period

time

P(u)= ?

1 week

Recommendations = ?

> 5 relevant tweets per user

5529 candidate news articles

user profile trend profile


Results: Which weighting functions is best for generating trend profiles?

!"#$

!"##$

!"#%$

!"#&$

!"#'$

!"#($

!"#)$

!"#*$

!"#+$

,-$ ,-./0-$ 12,-$ 12,-./0-$

344$

56($

Time sensitive weighting function performs best!


Results: Can we improve recommendation by combining trend and user profiles?

!"##$

!"##%$

!"#&$

!"#&%$

!"#'$

!"#'%$

!"#($

!$ !"&$ !"($ !")$ !"*$ #$

!""

#

$%&%'()(&#*#+,&#-,'./0%1,0#

+,-./0,123-4#!!$

+,-./0,123-4%!!$

+,-./0,123-4*!!$

Aggregation of trend and user profiles improve the

recommendation


Conclusions and Future Work

• Trend and user modeling framework for personalized news recommendations

• Analysis:

•  User profiles change over time influenced by trends •  Appropriate concept weighting strategies allow for the discovery of local trends

• Evaluation: •  Time sensitive weighting function is best for generating trend profiles •  Aggregation of trend and user profile can improve the performance of

recommendations

•  Future work: What’s the impact of profiles from different domains on the performance of recommendations?


Thank you!

Qi Gao, Fabian Abel, Geert-Jan Houben, Ke Tao

Twitter: @persweb http://wis.ewi.tudelft.nl/tweetum/


Reference

•  Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web. In ESWC2011, Heraklion, Crete, Greece, May 2011.

•  Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web. WebSci'11, Koblenz, Germany, June 2011.

•  Analyzing User Modeling on Twitter for Personalized News Recommendation. UMAP2011, Girona, Spain, July 2011.

•  http://wis.ewi.tudelft.nl/tums/

interweaving trend and user modeling for personalized news recommendation

Technology

interweaving

united states

weight

gt

amp

concepts

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