semantics2016 - exploring dynamics and semantics of user interests for user modeling on twitter for...
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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_duossubject
yago:BluesRockMusicianstype
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
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User Modeling Framework
semantic interest
propagation
temporal dynamics
• category
• …
• category & property
• Ahmed
• …
• OrlandiUser 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
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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
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Semantic Interest Propagation
Core propagation strategies
• property-based
P: property count in DBpedia graph
Combine different semantics
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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
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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
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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
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Compare to State-of-Art
outperform baselines
best: um(CF-IDF, none,
category & property)
Conclusions & Future Work
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• 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
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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