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 dierent 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 eect of those dimensions for user modeling. In this paper, we address this research gap by investigating 16 different user modeling strategies produced by various combinations of those dimensions. Dierent 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 eect on user modeling in the case of leveraging a rich Interest Representation or considering Content Enrichment.

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

5. 5
Dimensions
representation
Bag of
Words
Topic
Modeling
Bag of
Concepts
Mixed
Approach
Background – User Modeling

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

19. Results
synset & concept > concept

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