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Guangyuan Piao, John G. Breslin
Unit for Social
Semantics
12th International Conference on Semantic Systems
Leipzig, Germany, 12-15, September, 2016
Exploring Dynamics and Semantics of
User Interests for User Modeling on Twitter for
Link Recommendations
2
1/3 users seek medical information
and over 50% users consume news
on Social Networks
Facebook and Twitter together generate
more than 5 billion microblogs / day
[SOURCE] Semantic Filtering for Social Data, Amit et al., Internet Computing’16
Background – User Modeling
content enrichment
analysis &
user modeling
interest profile
?
personalized content
recommendations
(How) can we infer
user interest profiles
that support the
content recommender?
3[SOURCE] Analyzing User Modeling on Twitter for Personalized News Recommendations, UMAP’11
Background – User Modeling
Representation of User Interest
Bag of
Words
Topic
Modeling
Bag of
Concepts
users' interests are
represented as a
set of words
topics: co-occurring words
document: mixture of topics
users' interests are
represented as a
set of concepts
• can exploit background
knowledge about concepts
for interest propagation
• focus on words
• assumption: a single doc contains rich information
• cannot provide semantic relationships among words
Bag-of-Concepts
dbpedia:The_Black_Keys
dbpedia:Eagles_of_Death_Metal
Background – User Modeling
dbpedia:The_Wombats
Weighting Scheme: importance of a concept for user
dbpedia:The_Black_Keys (3)
dbpedia:Eagles_of_Death_Metal (5)
Background – User Modeling
dbpedia:The_Wombats (2)
Concept Frequency (CF)
Semantic Interest Propagation
• different structures of DBpedia beyond category information are
not fully explored
Related Work – Semantics
dbpedia:The_Black_Keys
dbpedia:The_Wombats
dbc:Rock_music_duos
dbpedia:Indie_rock
subject
genredbpedia:The_Black_Keys
Temporal Dynamics of User Interests
• assumption: user interests might change over time
• no comparative evaluation over different methods
Related Work – Dynamics
long-term user profile
short-term user profile
interest decay function
historical user-generated content (UGC)
(e.g., the last two weeks UGC)
Concepts
• entities, categories and classes from DBpedia which can be used
for representing user interests
Definition
dbpedia:The_Black_Keys
dbc:Rock_music_duos
subject
yago:BluesRockMusicians
type
entity
category
class
CF-IDF: Concept Frequency – Inverse Document Frequency
• Weighting Scheme: CF-IDF vs. CF
• Semantics: explore different structures of DBpedia
• Dynamics: comparative study on different methods
Aim of Work
We propose and evaluate user modeling strategies
using best-performing strategies in the three dimensions
11
User Modeling Framework
semantic interest
propagation
temporal dynamics
• category
• …
• category & property
• Ahmed
• …
• Orlandi
User Profiles
P(u)
Google Category:
Smartphones
… iPhone
0.09 0.12 … 0.08
a concept-based profile P(u)
weighting
scheme
entity-based
user profiles
12
Core propagation strategies
• category-based
SP: sub-pages of the category
SC: sub-categories of the category
• class-based
SP’: sub-pages of the class
SC’: sub-classes of the class
Semantic Interest Propagation
13
Semantic Interest Propagation
Core propagation strategies
• property-based
P: property count in DBpedia graph
Combine different semantics
14
Dynamics of User Interests
Interest decay functions
• Long-term(Orlandi) [SEMANTiCS]
• Long-term(Ahmed) [SIGKDD]
Long-term(Ahmedα): μ2week, μ2month, μall
• Long-term(Abel) [WebSci]
μweek = μ = e -1
μmonth = μ 2
μall = μ 3
Dataset
• 322 users: shared at least one link in the last two weeks
• 247,676 tweets in total
Experiment
• task: recommending 10 links (URLs)
• recommendation algorithm: cosine similarity(P(u), P(i))
P(i): item (link) profile using the same modeling strategy for P(u)
• ground truth links: links shared in the last two weeks
• candidate links: 15,440 links
15
Experiment Setup
used for user modeling
ground truth
links (URLs)
recommendation time
Results
16
Study of Weighting Scheme
using CF-IDF improves the performance significantly (<.05)
# of concepts after propagation
17
Semantic Interest Propagation
• on average, 224 concepts before extension
• 1,865, 1,317 and 1,152 respectively after extension
Recommendation Results
18
Semantic Interest Propagation
combining different structures of information improves the
performance in the context of link recommendations
Results
19
A Comparative Study of Dynamics
Ahmed’s and Orlandi’s methods
provide competitive performance
in line with previous studies,
using interest decay functions
improves the performance
20
Our User Modeling Strategies
extension strategy
using DBpedia
temporal dynamics
• category & property
• Ahmedα
Google Category:
Smartphones
… iPhone
0.09 0.12 … 0.08
a concept-based profile P(u)
weighting
scheme
(CF-IDF)
entity-based
user profiles
um(weighting scheme, temporal dynamics, propagation strategy)
User Profiles
P(u)
Results
21
Compare to State-of-Art
outperform baselines
best: um(CF-IDF, none,
category & property)
Conclusions & Future Work
22
• CF-IDF & combining different structures of DBpedia are beneficial
- um(CF-IDF, none, category & property) performs best
• Ahmed’s and Orlandi’s methods provide competitive performance
for capturing dynamics of user interests
• investigation of combining different dimensions of user modeling
• richer interest representation beyond concepts for users
23
Thank you for your attention!
Guangyuan Piao
homepage: http://parklize.github.io
e-mail: guangyuan.piao@insight-centre.org
twitter: https://twitter.com/parklize
slideshare: http://www.slideshare.net/parklize

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SEMANTiCS2016 - Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

  • 1. Guangyuan Piao, John G. Breslin Unit for Social Semantics 12th International Conference on Semantic Systems Leipzig, Germany, 12-15, September, 2016 Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations
  • 2. 2 1/3 users seek medical information and over 50% users consume news on Social Networks Facebook and Twitter together generate more than 5 billion microblogs / day [SOURCE] Semantic Filtering for Social Data, Amit et al., Internet Computing’16
  • 3. Background – User Modeling content enrichment analysis & user modeling interest profile ? personalized content recommendations (How) can we infer user interest profiles that support the content recommender? 3[SOURCE] Analyzing User Modeling on Twitter for Personalized News Recommendations, UMAP’11
  • 4. Background – User Modeling Representation of User Interest Bag of Words Topic Modeling Bag of Concepts users' interests are represented as a set of words topics: co-occurring words document: mixture of topics users' interests are represented as a set of concepts • can exploit background knowledge about concepts for interest propagation • focus on words • assumption: a single doc contains rich information • cannot provide semantic relationships among words
  • 6. Weighting Scheme: importance of a concept for user dbpedia:The_Black_Keys (3) dbpedia:Eagles_of_Death_Metal (5) Background – User Modeling dbpedia:The_Wombats (2) Concept Frequency (CF)
  • 7. Semantic Interest Propagation • different structures of DBpedia beyond category information are not fully explored Related Work – Semantics dbpedia:The_Black_Keys dbpedia:The_Wombats dbc:Rock_music_duos dbpedia:Indie_rock subject genredbpedia:The_Black_Keys
  • 8. Temporal Dynamics of User Interests • assumption: user interests might change over time • no comparative evaluation over different methods Related Work – Dynamics long-term user profile short-term user profile interest decay function historical user-generated content (UGC) (e.g., the last two weeks UGC)
  • 9. Concepts • entities, categories and classes from DBpedia which can be used for representing user interests Definition dbpedia:The_Black_Keys dbc:Rock_music_duos subject yago:BluesRockMusicians type entity category class
  • 10. CF-IDF: Concept Frequency – Inverse Document Frequency • Weighting Scheme: CF-IDF vs. CF • Semantics: explore different structures of DBpedia • Dynamics: comparative study on different methods Aim of Work We propose and evaluate user modeling strategies using best-performing strategies in the three dimensions
  • 11. 11 User Modeling Framework semantic interest propagation temporal dynamics • category • … • category & property • Ahmed • … • Orlandi User Profiles P(u) Google Category: Smartphones … iPhone 0.09 0.12 … 0.08 a concept-based profile P(u) weighting scheme entity-based user profiles
  • 12. 12 Core propagation strategies • category-based SP: sub-pages of the category SC: sub-categories of the category • class-based SP’: sub-pages of the class SC’: sub-classes of the class Semantic Interest Propagation
  • 13. 13 Semantic Interest Propagation Core propagation strategies • property-based P: property count in DBpedia graph Combine different semantics
  • 14. 14 Dynamics of User Interests Interest decay functions • Long-term(Orlandi) [SEMANTiCS] • Long-term(Ahmed) [SIGKDD] Long-term(Ahmedα): μ2week, μ2month, μall • Long-term(Abel) [WebSci] μweek = μ = e -1 μmonth = μ 2 μall = μ 3
  • 15. Dataset • 322 users: shared at least one link in the last two weeks • 247,676 tweets in total Experiment • task: recommending 10 links (URLs) • recommendation algorithm: cosine similarity(P(u), P(i)) P(i): item (link) profile using the same modeling strategy for P(u) • ground truth links: links shared in the last two weeks • candidate links: 15,440 links 15 Experiment Setup used for user modeling ground truth links (URLs) recommendation time
  • 16. Results 16 Study of Weighting Scheme using CF-IDF improves the performance significantly (<.05)
  • 17. # of concepts after propagation 17 Semantic Interest Propagation • on average, 224 concepts before extension • 1,865, 1,317 and 1,152 respectively after extension
  • 18. Recommendation Results 18 Semantic Interest Propagation combining different structures of information improves the performance in the context of link recommendations
  • 19. Results 19 A Comparative Study of Dynamics Ahmed’s and Orlandi’s methods provide competitive performance in line with previous studies, using interest decay functions improves the performance
  • 20. 20 Our User Modeling Strategies extension strategy using DBpedia temporal dynamics • category & property • Ahmedα Google Category: Smartphones … iPhone 0.09 0.12 … 0.08 a concept-based profile P(u) weighting scheme (CF-IDF) entity-based user profiles um(weighting scheme, temporal dynamics, propagation strategy) User Profiles P(u)
  • 21. Results 21 Compare to State-of-Art outperform baselines best: um(CF-IDF, none, category & property)
  • 22. Conclusions & Future Work 22 • CF-IDF & combining different structures of DBpedia are beneficial - um(CF-IDF, none, category & property) performs best • Ahmed’s and Orlandi’s methods provide competitive performance for capturing dynamics of user interests • investigation of combining different dimensions of user modeling • richer interest representation beyond concepts for users
  • 23. 23 Thank you for your attention! Guangyuan Piao homepage: http://parklize.github.io e-mail: guangyuan.piao@insight-centre.org twitter: https://twitter.com/parklize slideshare: http://www.slideshare.net/parklize

Editor's Notes

  1. - focus on words, which cannot provide semantic information and relationships among them
  2. - focus on words, which cannot provide semantic information and relationships among them
  3. - focus on words, which cannot provide semantic information and relationships among them
  4. - focus on words, which cannot provide semantic information and relationships among them
  5. What is the interest propagation ? Give def.
  6. What is the interest propagation ? Give def.
  7. What is the interest propagation ? Give def.
  8. What is the interest propagation ? Give def.
  9. Mu square, mu cube