ekaw2016 - interest representation, enrichment, dynamics, and propagation: a study of the synergetic...
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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
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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
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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
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Background – User Modeling
Dimensions
representation enrichment
propagation dynamics
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Dimensions
representation
Bag of Words
Topic Modeling
Bag of Concepts
Mixed Approach
Background – User Modeling
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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)
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Background – User Modeling
Dimensions
enrichment
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Background – User Modeling
Dimensions
dynamics
Assumption: user interests might change over time
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Background – User Modeling
Dimensions
propagation
dbpedia:The_Wombats
dbpedia:Indie_rock genre
dbpedia:The_Black_Keys
dbc:Rock_music_duos
subject
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Background – User Modeling
Dimensions
representation enrichment
propagation dynamics
dimensions have been studied separately
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Aim of Work
representation enrichment
propagation dynamics
Dimensions
to investigate (how) can we combine different dimensions for user modeling
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User Modeling Framework
user interest profiles
entity extraction
primitive interests IF weighting
temporal dynamics interest propagation
primitive & propagated
interests
synset extraction
optional enabled
enrichment
IDF weighting normalization
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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
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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
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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
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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)
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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
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Experiment Setup
used for user modeling
ground truth links (URLs)
recommendation time
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Results with enrichment > without enrichment
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Results
synset & concept > concept
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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 )
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Thank you for your attention!
Guangyuan Piao homepage: http://parklize.github.io e-mail: [email protected] twitter: https://twitter.com/parklize slideshare: http://www.slideshare.net/parklize