UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
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Transcript of UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
DelftUniversity ofTechnology
Analyzing User Modeling on Twitter for Personalized News RecommendationsUMAP, Girona, July 13, 2011
Fabian Abel, Qi Gao, Geert-Jan Houben, Ke TaoWeb Information Systems, TU Delft
2Analyzing User Modeling on Twitter for Personalized News Recommendations
The Social Web
Help me to tackle the
information overload!
Recommend me news articles
that now interest me!
Help me to find interesting (social) media!
Do not bother me with
advertisements that are not
interesting for me!
Give me personalized
support when I do my online training!
Who is this? What are his personal demands? How can we make him happy?
Personalize my Web
experience!
3Analyzing User Modeling on Twitter for Personalized News Recommendations
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
4Analyzing User Modeling on Twitter for Personalized News Recommendations
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 the news recommender?
5Analyzing User Modeling on Twitter for Personalized News Recommendations
User Modeling FrameworkBuilding Blocks for generating valuable 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
6Analyzing User Modeling on Twitter for Personalized News Recommendations
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
7Analyzing User Modeling on Twitter for Personalized News Recommendations
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
time
June 27 July 4 July 11
8Analyzing User Modeling on Twitter for Personalized News Recommendations
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
9Analyzing User Modeling on Twitter for Personalized News Recommendations
User Modeling Building Blocks
Profile? concept weight
2. Profile Type
4. How to weight the concepts?
1. Temporal
Constraints
3. Semantic
Enrichment
Francesca Schiavone
French OpenTennis
4. Weighting Scheme
time
June 27 July 4 July 11
?
weight(Francesca Schiavone)
Concept frequency
4
weight(French Open)
weight(Tennis)
36
10Analyzing User Modeling on Twitter for Personalized News Recommendations
(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
11Analyzing User Modeling on Twitter for Personalized News Recommendations
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
12Analyzing User Modeling on Twitter for Personalized News Recommendations
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
13Analyzing User Modeling on Twitter for Personalized News Recommendations
Size of user profiles
entity-based
topic-basedhashtag-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
14Analyzing User Modeling on Twitter for Personalized News Recommendations
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
15Analyzing User Modeling on Twitter for Personalized News Recommendations
?
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(
0.5
0.5
0
⎛
⎝
⎜ ⎜ ⎜
⎞
⎠
⎟ ⎟ ⎟,
0.5
0
0.5
⎛
⎝
⎜ ⎜ ⎜
⎞
⎠
⎟ ⎟ ⎟) =1
music
football
tennis
old new
Hashtag-based profiles change stronger than entity-based and topic-based profiles
#
T
The older the profile the more it differs from the current profile
Temporal Constraint
s
16Analyzing User Modeling on Twitter for Personalized News Recommendations
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
17Analyzing User Modeling on Twitter for Personalized News Recommendations
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
18Analyzing User Modeling on Twitter for Personalized News Recommendations
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?
19Analyzing User Modeling on Twitter for Personalized News Recommendations
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
20Analyzing User Modeling on Twitter for Personalized News Recommendations
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
21Analyzing User Modeling on Twitter for Personalized News Recommendations
Impact of Semantic Enrichment
Tweet-based
Further enrichment
Further semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles!
T
Semantic Enrichment
22Analyzing User Modeling on Twitter for Personalized News Recommendations
Impact of temporal characteristics
Selection 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
23Analyzing User Modeling on Twitter for Personalized News Recommendations
Conclusions and Future Work
• What we did: Twitter-based User Modeling for Recommending News Articles
• Analysis: • Semantic enrichment results in richer user profiles (quantitative)• User interest profiles change over time (hashtag-based stronger
than others)• Weekend/weekday pattern more significant than day/night pattern
• Evaluation:• Best user modeling strategy: Entity-based > topic-based > hashtag-
based • Semantic enrichment improves recommendation quality• Adapting to temporal context helps for topic-based strategy
• Future work: for what type of personalization tasks can we exploit what type of Twitter profiles?
24Analyzing User Modeling on Twitter for Personalized News Recommendations
Thank you!
Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao
Twitter: @perswebhttp://persweb.org/ http://u-sem.org/
25Analyzing User Modeling on Twitter for Personalized News Recommendations
Research Questions
1. What type of user interest profiles can we infer from Twitter activities?
2. Can we exploit Twitter-based profiles for personalizing users’ Social Web experience?
interest
?
Personalized news recommendationsin time:
time?
time
Good Morning! #tooearly
I like this http://bit.ly/5d4r2t
Why do people now blame Julian Assange?
Ajax deserves it! #sport
26Analyzing User Modeling on Twitter for Personalized News Recommendations
I like this http://bit.ly/4Gfd2
Analyzing Twitter-based Profiles for Personalized News Recommendations (in time)
Analysis and User Modeling
tweets
Semantic Enrichment, Linkage, Alignment
Francesca Schiavone is great!
Thirty in women'stennis is primordially old, an age when agility and desire recedes as the next wave of younger/faster/stronger players encroaches. It's uncommon for any athlete to have a breakthrough season at 30, but it's exceedingly…
topic:Tennis
oc:Sportsevent:FrenchOpen
dbpedia:Schiavone
Interests:TennisFootball
interest
time
Personalized news recommendations
News Recommendations in time:
interest
time
Ajax gives De Jong a breakAjax manager Frank deBoer announced that…
Nice, thank you!
27Analyzing User Modeling on Twitter for Personalized News Recommendations
User Modeling Challenge
Profile?
Personalized news recommender
I want my personalized
news recommendatio
ns!
Wednesday, July 13th 2011, 9:10am
(How) can we infer a Twitter-based user profile that
supports the news recommender?
?
28Analyzing User Modeling on Twitter for Personalized News Recommendations
time
Bob tweets…
Fr, 6am
Good Morning! #tooearly
Why do people now blame Julian Assange?
Fr, 3pm Fr, 8pm
I like this http://bit.ly/5d4r2t
Sa, 5pm
Ajax deserves it! #sport
People publish more than 60 million tweets per day!