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Guangyuan Piao, John G. Breslin
Unit for Social Semantics
20th International Conference on Knowledge Engineering and Knowledge Management
Bologna, Italy, 19-23, November, 2016
Interest Representation, Enrichment, Dynamics, and
Propagation: A Study of the Synergetic Effect of
Different User Modeling Dimensions for Personalized
Recommendations on Twitter
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
4
Background – User Modeling
Dimensions
representation enrichment
propagation dynamics
5
Dimensions
representation
Bag of
Words
Topic
Modeling
Bag of
Concepts
Mixed
Approach
Background – User Modeling
Bag-of-Concepts example
dbpedia:The_Black_Keys (3)
dbpedia:Eagles_of_Death_Metal (5)
Background – User Modeling
dbpedia:The_Wombats (2)
Interest Frequency (IF)
7
Background – User Modeling
Dimensions
enrichment
8
Background – User Modeling
Dimensions
dynamics
Assumption:
user interests might
change over time
Background – User Modeling
Dimensions
propagation
dbpedia:The_Wombats
dbpedia:Indie_rockgenre
dbpedia:The_Black_Keys
dbc:Rock_music_duos
subject
10
Background – User Modeling
Dimensions
representation enrichment
propagation dynamics
dimensions have been studied separately
11
Aim of Work
representation enrichment
propagation dynamics
Dimensions
to investigate (how) can we
combine different dimensions for user modeling
12
User Modeling Framework
user interest
profiles
entity extraction
primitive
interestsIF weighting
temporal dynamics
interest propagation
primitive
& propagated
interests
synset extraction
optional enabled
enrichment
IDF weightingnormalization
13
Representation
•  concept-based
!  DBpedia concepts are extracted using Aylien API
•  mixed approach (WordNet synset & concept-based)
!  synsets are extracted using Degemmis’s method [UMUAI]
Enrichment
•  exploring embedded URL in tweets
!  concepts or synsets are extracted from the content of URL
Interest Representation & Enrichment
14
Propagation strategy using DBpedia
•  category-based
SP: sub-pages of the category
SC: sub-categories of the category
•  property-based
P: property count in DBpedia graph
Interest Propagation
15
Temporal 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
16
Design Space of User Modeling
The design space of user modeling, spanning
2x2x2x2=16 possible user modeling strategies.
Notation
•  um( representation; enrichment; dynamics; semantics )
•  use “none” to denote a certain dimension is disabled
!  um( synset & concept; enrichment; none; none)
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
17
Experiment Setup
used for user modeling
ground truth
links (URLs)
recommendation time
Results
with enrichment > without enrichment
Results
synset & concept > concept
Conclusions & Future Work
•  propagation helps
when using concept-based representation without enrichment
•  the most important dimensions :
Content Enrichment & Interest Representation
•  investigation of how different percentages of links affect the performance
•  the best-performing strategy :
um (synset & concept; enrichment; dynamics; none )
21
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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EKAW2016 - Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter

  • 1. Guangyuan Piao, John G. Breslin Unit for Social Semantics 20th International Conference on Knowledge Engineering and Knowledge Management Bologna, Italy, 19-23, November, 2016 Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter
  • 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. 4 Background – User Modeling Dimensions representation enrichment propagation dynamics
  • 6. Bag-of-Concepts example dbpedia:The_Black_Keys (3) dbpedia:Eagles_of_Death_Metal (5) Background – User Modeling dbpedia:The_Wombats (2) Interest Frequency (IF)
  • 7. 7 Background – User Modeling Dimensions enrichment
  • 8. 8 Background – User Modeling Dimensions dynamics Assumption: user interests might change over time
  • 9. Background – User Modeling Dimensions propagation dbpedia:The_Wombats dbpedia:Indie_rockgenre dbpedia:The_Black_Keys dbc:Rock_music_duos subject
  • 10. 10 Background – User Modeling Dimensions representation enrichment propagation dynamics dimensions have been studied separately
  • 11. 11 Aim of Work representation enrichment propagation dynamics Dimensions to investigate (how) can we combine different dimensions for user modeling
  • 12. 12 User Modeling Framework user interest profiles entity extraction primitive interestsIF weighting temporal dynamics interest propagation primitive & propagated interests synset extraction optional enabled enrichment IDF weightingnormalization
  • 13. 13 Representation •  concept-based !  DBpedia concepts are extracted using Aylien API •  mixed approach (WordNet synset & concept-based) !  synsets are extracted using Degemmis’s method [UMUAI] Enrichment •  exploring embedded URL in tweets !  concepts or synsets are extracted from the content of URL Interest Representation & Enrichment
  • 14. 14 Propagation strategy using DBpedia •  category-based SP: sub-pages of the category SC: sub-categories of the category •  property-based P: property count in DBpedia graph Interest Propagation
  • 15. 15 Temporal 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
  • 16. 16 Design Space of User Modeling The design space of user modeling, spanning 2x2x2x2=16 possible user modeling strategies. Notation •  um( representation; enrichment; dynamics; semantics ) •  use “none” to denote a certain dimension is disabled !  um( synset & concept; enrichment; none; none)
  • 17. 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 17 Experiment Setup used for user modeling ground truth links (URLs) recommendation time
  • 18. Results with enrichment > without enrichment
  • 20. Conclusions & Future Work •  propagation helps when using concept-based representation without enrichment •  the most important dimensions : Content Enrichment & Interest Representation •  investigation of how different percentages of links affect the performance •  the best-performing strategy : um (synset & concept; enrichment; dynamics; none )
  • 21. 21 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