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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
3 Interweaving Trend and User Modeling
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
4 Interweaving Trend and User Modeling
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
5 Interweaving Trend and User Modeling
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
6 Interweaving Trend and User Modeling
Trend and User Modeling Framework
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
7 Interweaving Trend and User Modeling
Trend and User Modeling Framework
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
8 Interweaving Trend and User Modeling
Trend and User Modeling Framework
3. How to weight the concepts?
time
Nov 30 Dec 15 Dec 30
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) <
9 Interweaving Trend and User Modeling
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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#%('#(#!'!"
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)!('#(#!'!"
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"$*&+!
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503+467-"
The trending entities within one week (TF)
emphasize the emerging popular entities (time
sensitive TF*IDF)
Obituary: Leslie Nielsen
WikiLeaks founder on Interpol most
wanted list
Tiny Qatar will host the
World Cup
Weighting Scheme
10 Interweaving Trend and User Modeling
Trend and User Modeling Framework
time
Nov 30 Dec 15 Dec 30
Semantic Enrichment
Profile Type
Weighting Scheme
Nov 15
4. How to combine trend and user profiles?
Aggregation Trend Profile
User Profile long term user history
current trends
d*
(1-d)*
aggregated profile
11 Interweaving Trend and User Modeling
Experiment: News Recommendation • Task: Recommending news articles (= tweets with URLs pointing to news
articles)
• Dataset: > 2month; >10m tweets; > 20k users
• Recommender algorithm: cosine similarity between profile and candidate item
• Ground truth: (re-)tweets of users (577 users)
• Candidate items: news-related tweets posted during evaluation period
time
P(u)= ?
1 week
Recommendations = ?
> 5 relevant tweets per user
5529 candidate news articles
user profile trend profile
12 Interweaving Trend and User Modeling
Results: Which weighting functions is best for generating trend profiles?
!"#$
!"##$
!"#%$
!"#&$
!"#'$
!"#($
!"#)$
!"#*$
!"#+$
,-$ ,-./0-$ 12,-$ 12,-./0-$
344$
56($
Time sensitive weighting function performs best!
13 Interweaving Trend and User Modeling
Results: Can we improve recommendation by combining trend and user profiles?
!"##$
!"##%$
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!"#&%$
!"#'$
!"#'%$
!"#($
!$ !"&$ !"($ !")$ !"*$ #$
!""
#
$%&%'()(&#*#+,&#-,'./0%1,0#
+,-./0,123-4#!!$
+,-./0,123-4%!!$
+,-./0,123-4*!!$
Aggregation of trend and user profiles improve the
recommendation
14 Interweaving Trend and User Modeling
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?
15 Interweaving Trend and User Modeling
Thank you!
Qi Gao, Fabian Abel, Geert-Jan Houben, Ke Tao
Twitter: @persweb http://wis.ewi.tudelft.nl/tweetum/
16 Interweaving Trend and User Modeling
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/