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Guangyuan Piao, John G. Breslin
Unit for Social Semantics
20th International Conference on Knowledge Engineering and Knowl...
2
1/3 users seek medical information
and over 50% users consume news
on Social Networks
Facebook and Twitter together gene...
Background – User Modeling
content enrichment
analysis &
user modeling
interest profile
?
personalized content
recommendat...
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:Th...
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:R...
10
Background – User Modeling
Dimensions
representation enrichment
propagation dynamics
dimensions have been studied separ...
11
Aim of Work
representation enrichment
propagation dynamics
Dimensions
to investigate (how) can we
combine different dim...
12
User Modeling Framework
user interest
profiles
entity extraction
primitive
interestsIF weighting
temporal dynamics
inte...
13
Representation
•  concept-based
!  DBpedia concepts are extracted using Aylien API
•  mixed approach (WordNet synset & ...
14
Propagation strategy using DBpedia
•  category-based
SP: sub-pages of the category
SC: sub-categories of the category
•...
15
Temporal Dynamics of User Interests
Interest decay functions
•  Long-term(Orlandi) [SEMANTiCS]
•  Long-term(Ahmed) [SIG...
16
Design Space of User Modeling
The design space of user modeling, spanning
2x2x2x2=16 possible user modeling strategies....
Dataset
•  322 users: shared at least one link in the last two weeks
•  247,676 tweets in total
Experiment
•  task: recomm...
Results
with enrichment > without enrichment
Results
synset & concept > concept
Conclusions & Future Work
•  propagation helps
when using concept-based representation without enrichment
•  the most impo...
21
Thank you for your attention!
Guangyuan Piao
homepage: http://parklize.github.io
e-mail: guangyuan.piao@insight-centre....
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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

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Microblogging services such as Twitter have been widely
adopted due to the highly social nature of interactions they have facilitated. With the rich information generated by users on these services, user modeling aims to acquire knowledge about a user's interests, which is a fundamental step towards personalization as well as recommendations. To this end, researchers have explored di erent dimensions such as (1) Interest Representation, (2) Content Enrichment, (3) Temporal Dynamics of user interests, and (4) Interest Propagation using semantic information from a knowledge base such as DBpedia. However, those dimensions of user modeling have largely been studied separately, and there
is a lack of research on the synergetic e ect of those dimensions for user modeling. In this paper, we address this research gap by investigating 16 diff erent user modeling strategies produced by various combinations of those dimensions. Di erent user modeling strategies are evaluated in the context of a personalized link recommender system on Twitter. Results show that Interest Representation and Content Enrichment play crucial roles in user modeling, followed by Temporal Dynamics. The user mod-
eling strategy considering Interest Representation, Content Enrichment and Temporal Dynamics provides the best performance among the 16 strategies. On the other hand, Interest Propagation has little e ect on user modeling in the case of leveraging a rich Interest Representation or considering Content Enrichment.

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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. 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. 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. 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. 4 Background – User Modeling Dimensions representation enrichment propagation dynamics
  5. 5. 5 Dimensions representation Bag of Words Topic Modeling Bag of Concepts Mixed Approach Background – User Modeling
  6. 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. 7 Background – User Modeling Dimensions enrichment
  8. 8. 8 Background – User Modeling Dimensions dynamics Assumption: user interests might change over time
  9. 9. Background – User Modeling Dimensions propagation dbpedia:The_Wombats dbpedia:Indie_rockgenre dbpedia:The_Black_Keys dbc:Rock_music_duos subject
  10. 10. 10 Background – User Modeling Dimensions representation enrichment propagation dynamics dimensions have been studied separately
  11. 11. 11 Aim of Work representation enrichment propagation dynamics Dimensions to investigate (how) can we combine different dimensions for user modeling
  12. 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. 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. 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. 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. 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. 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. 18. Results with enrichment > without enrichment
  19. 19. Results synset & concept > concept
  20. 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. 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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