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

Delft University of Technology What we do: Science and Engineering for the Personal Web domains: news social media cultural heritage public data e-learning Personalized Personalized Adaptive Systems Recommendations Search

Analysis and User Modeling

Semantic Enrichment, Linkage and Alignment

user/usage data

Social Web

Interweaving Trend and User Modeling 2 Research Challenge

Personalized News Recommender

trends time Profile

Nov 15 Nov 30 Dec 15 Dec 30 ? interested in:

Analysis and User Modeling politics people

Semantic Enrichment, (How) can we construct Twitter-based Linkage and Alignment profiles to support news recommenders?

(How) do trends influence personalized news recommendations?

Interweaving Trend and User Modeling 3 Twitter-based Trend and User Modeling Framework

Profile Type user’s interests Semantic Profile Enrichment news Twitter posts time ? recommender Weighting Scheme trends Aggregation

Interweaving Trend and User Modeling 4 Trend and User Modeling Framework Profile Type Interpol T Politics Profile? concept weight Interpol looking for this entity-based person http://bit.ly/pGnwkK ? T topic-based

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

time

June 27 July 4 July 11

Interweaving Trend and User Modeling 5 Trend and User Modeling Framework Profile Type Interpol (a) tweet-based

Profile? Semantic concept weight Enrichment Interpol looking for this Interpol person http://http://bit.ly/pGnwkKbit.ly/pGnwkK wikileaks Julian Assange

wikileaks (b) linkage enrichment WikiLeaks founder Julian Assange on Interpol most Julian Assange wanted list

2. Further enrich the semantics of tweets?

Interweaving Trend and User Modeling 6 Trend and User Modeling Framework Profile Type 3. How to weight the concepts? Semantic Enrichment TF Weighting Scheme

weight(wikileaks) weight(Julian Assange)

weight(Interpol)

time

Nov 15 Nov 30 Dec 15 Dec 30

Interweaving Trend and User Modeling 7 Trend and User Modeling Framework Profile Type 3. How to weight the concepts? Semantic Enrichment TF - Time sensitive weighting Time functions: smoothing the Sensitive TF*IDF weights with standard Weighting deviation Scheme

σ(interpol) < σ(united states) weight(interpol) > weight(united states)

time

Nov 15 Nov 30 Dec 15 Dec 30

Interweaving Trend and User Modeling 8 3. How does the weighting scheme Weighting impact trend profiles? Scheme

Theemphasize trending theentities emerging within popularone weekentities (TF) (time sensitive TF*IDF)

'&!!"'&!!" '%!!"'%!!"

'$!!"'$!!" Obituary: Leslie '#!!"'#!!" Nielsen '!!!"'!!!" Tiny Qatar *+,-./"0-1-.2" WikiLeaks founder will host the &!!"&!!" on Interpol most World Cup wanted list *03" %!!" *+,-.+"/.+-,+0" %!!" 4.5678,91+":1;-<" $!!"$!!" 12324" =.28,.">,.82.+"

!"#$%&'()"$*&+!,&-.%& 503+467-" !"#$%&'()"$*&+!,&-.%& #!!"#!!" ?1-1;" !"!" @+-.;5A8" '$(''(#!'!" '%(''(#!'!" '&(''(#!'!" #!(''(#!'!" ##(''(#!'!" #$(''(#!'!" #%(''(#!'!" #&(''(#!'!" )!(''(#!'!" '!('#(#!'!" '#('#(#!'!" '$('#(#!'!" '%('#(#!'!" '&('#(#!'!" #!('#(#!'!" ##('#(#!'!" #$('#(#!'!" #%('#(#!'!" #&('#(#!'!" )!('#(#!'!" !#('#(#!'!" !$('#(#!'!" !%('#(#!'!" !&('#(#!'!" !'(!'(#!''" '$(''(#!'!" '%(''(#!'!" '&(''(#!'!" #!(''(#!'!" ##(''(#!'!" #$(''(#!'!" #%(''(#!'!" #&(''(#!'!" )!(''(#!'!" !#('#(#!'!" !$('#(#!'!" !%('#(#!'!" !&('#(#!'!" '!('#(#!'!" '#('#(#!'!" '$('#(#!'!" '%('#(#!'!" '&('#(#!'!" #!('#(#!'!" ##('#(#!'!" #$('#(#!'!" #%('#(#!'!" #&('#(#!'!" )!('#(#!'!" !'(!'(#!''" #/!&

Interweaving Trend and User Modeling 9 Trend and User Modeling Framework Profile Type

4. How to combine trend Semantic and user profiles? Enrichment Weighting Scheme long term user history d* User Profile

current trends Aggregation (1-d)* Trend Profile

aggregated profile

time

Nov 15 Nov 30 Dec 15 Dec 30

Interweaving Trend and User Modeling 10 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 > 5 relevant • Ground truth: (re-)tweets of users (577 users) tweets per user • Candidate items: news-related tweets posted during evaluation period 5529 candidate news articles Recommendations = ? trend profile P(u)= ? user profile

time 1 week

Interweaving Trend and User Modeling 11 Results: Which weighting functions is best for generating trend profiles?

Time sensitive weighting !"#+$ function performs best! !"#*$ !"#)$ !"#($ !"#'$ 344$ !"#&$ 56($ !"#%$ !"##$ !"#$ ,-$ ,-./0-$ 12,-$ 12,-./0-$

Interweaving Trend and User Modeling 12 Results: Can we improve recommendation by combining trend and user profiles?

Aggregation of trend and user profiles improve the recommendation

!"#($

!"#'%$

!"#'$

!"#&%$ !""# +,-./0,123-4#!!$ !"#&$ +,-./0,123-4%!!$ !"##%$ +,-./0,123-4*!!$

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

Interweaving Trend and User Modeling 13 Conclusions and Future Work

• Trend and user modeling framework for personalized news recommendations • Analysis:

• User profiles change over time à 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?

Interweaving Trend and User Modeling 14 Thank you!

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

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

Interweaving Trend and User Modeling 15 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 16