This document presents a framework for interweaving trend and user modeling to improve personalized news recommendations. The framework constructs profiles from a user's Twitter data and trends in topics discussed on Twitter. An experiment shows that combining trend and user profiles through time-sensitive weighting and aggregation outperforms using only user profiles for news recommendation. Future work will explore the impact of profiles from different domains on recommendation performance.

1. 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

2. 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

3. Research Challenge
Personalized News
Recommender
trends time
Profile
e?
? In flu enc Nov 15 Nov 30
interested in:
Dec 15 Dec 30
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

4. Twitter-based Trend and User Modeling Framework
Profile Type
user’s
interests
Semantic
Profile Enrichment
Twitter posts
time ? Weighting
news
recommender
cu
rre Scheme
o f nt t
co Tw wee
mm itt ts trends
un er
ity
Aggregation
Interweaving Trend and User Modeling 4

5. 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

6. Trend and User Modeling Framework
Profile Type
Interpol (a) tweet-based
Profile? Semantic
concept weight
Interpol
Enrichment
Interpol looking for this
person http://bit.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

7. 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

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

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emphasize entities within
9
Scheme
Weighting

10. Trend and User Modeling Framework
Profile Type
4. How to combine trend Semantic
Enrichment
and user profiles?
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

11. 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

12. 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

13. Results: Can we improve recommendation by
combining trend and user profiles?
Aggregation of trend and
user profiles improve the
recommendation
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Interweaving Trend and User Modeling 13

14. 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?
Interweaving Trend and User Modeling 14

15. 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

16. 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