Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations Guangyuan Piao, John G. Breslin Unit for Social Semantics 24 th Conference on User Modeling, Adaptation and Personalization Halifax, Canada, 13-16, July, 2016
UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations
Apr 12, 2017
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Analyzing Aggregated Semantics-enabled
User Modeling on Google+ and Twitter for
Personalized Link Recommendations
Guangyuan Piao, John G. Breslin
Unit for Social Semantics
24th Conference on User Modeling, Adaptation and Personalization Halifax, Canada, 13-16, July, 2016
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Help me to tackle the information
overload!
Recommend me news articles that now interest me!
Help me to find interesting
(social) media!
Do not bother me with advertisements that are
not interesting for me!Give me personalized support when I do my
online training!
Who is this? What are his personal demands? How can we make him happy?
Personalize my Social Web experience!
The Social Web
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
演示者
演示文稿备注
- focus on words, which cannot provide semantic information and relationships among them
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 are formed by groups of co-occurring words and each document is treated
as a mixture of topics
users' interests are represented as a set of concepts
• can exploit background knowledge about conceptsfor interest propagation
• focus on words• assumption: a single doc contains rich information• cannot provide semantic relationships among words
演示者
演示文稿备注
- focus on words, which cannot provide semantic information and relationships among them
Bag-of-Concepts
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dbpedia:The_Black_Keys
dbpedia:Eagles_of_Death_Metal
Background – User Modeling
演示者
演示文稿备注
- focus on words, which cannot provide semantic information and relationships among them
Aggregated User Interest Profiles
Interest Propagation
Related Work
flickr
Category:Indie_rock
The_Black_KeysEagles_of_Death_Metal genre
genre7 times more interests using category-based profiles
might be helpful in the context of recommender systems
delicious
stumbleupon twitter face
book
Abel et al. [UMUAI’13] Orlandi et al. [SEMANTiCS’12]same weight for each Online Social Network(OSN) profile
演示者
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What is the interest propagation ? Give def.
Aggregated User Interest Profiles
• to investigate if giving a higher weight to the targeted OSN for aggregation improves profiles without aggregation more significantly
Interest Propagation
• to study category-based user profiles in the context of recommender systems on Twitter compared to entity-based ones
• to propose and evaluate a mixed approach using entity- and category-based user interest profiles
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Aim of Work
演示者
演示文稿备注
What is the interest propagation ? Give def.
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User Modeling Framework
propagation strategyusing DBpedia
1. T(Cat)2. T(CatDiscount)3. Tonly+T(x)
User Profiles
Category:Smartphones
… iPhone
0.12 … 0.08ConceptFrequency
entity-baseduser profiles
normalization
Twitter & Google+ Dataset from about.me• 429 active users using Google+ and Twitter
Experiment• task: recommending 10 links (URLs)• recommendation algorithm: cosine similarity• ground truth links: 10 links shared via tweets• candidate links: 5,165 links
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Experiment Setup
used for user modeling ground truth
recommendation time
links (URLs)
Results - MRR
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Aggregated User Interest Profiles
• GmTn : m & n denote the weights for Google+ & Twitter• Tonly : entity-based Twitter profile without aggregation
a higher weight for the targeted OSN profile provides the best performance
Results - recall
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Aggregated User Interest Profiles
• GmTn : m & n denote the weights for Google+ & Twitter• Tonly : entity-based Twitter profile without aggregation
a higher weight for the targeted OSN profile provides the best performance
Category-based User Profiles
• T(Cat): replacing entities with the categories from DBpediaapplying the same weights
• T(CatDiscount): applies a discounting strategy for the extended categories
Entity- and Category-based User Profiles
• Tonly+T(x): combines the entity- and category-based profiles
Interest Propagation
SP: sub-pages SC: sub-categories
演示者
演示文稿备注
예제
Results - MRR
Interest Propagation
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Results - recall
Interest Propagation
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Conclusions & Future Work
Conclusions
• a higher weight for the targeted OSN for aggregation
• category-based user profiles does not outperform entity-based user profiles
• mixed approach outperforms entity- or category-based user profiles
Future Work
• in the near future, we plan to investigate different aspects of DBpedia for interest propagation
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Thank you for your attention!
Guangyuan Piao
homepage: http://parklize.github.ioe-mail: [email protected]: https://twitter.com/parklizeslideshare: http://www.slideshare.net/parklize
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