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Analyzing Aggregated Semantics-enabled
User Modeling on Google+ and Twitter for
Personalized Link Recommendations
Guangyua...
2
Help me to tackle
the information
overload!
Recommend me
news articles that
now interest me!
Help me to find
interesting...
Background – User Modeling
content enrichment
analysis &
user modeling
interest profile
?
personalized content
recommendat...
Background – User Modeling
 Representation of User Interest
Bag of
Words
Topic
Modeling
Bag of
Concepts
users' interests ...
 Bag-of-Concepts
5
dbpedia:The_Black_Keys
dbpedia:Eagles_of_Death_Metal
Background – User Modeling
 Aggregated User Interest Profiles
 Interest Propagation
Related Work
flickr
Category:Indie_rock
The_Black_Keys
Eagles_o...
 Aggregated User Interest Profiles
• to investigate if giving a higher weight to the targeted OSN for
aggregation improve...
8
User Modeling Framework
propagation strategy
using DBpedia
1. T(Cat)
2. T(CatDiscount)
3. Tonly+T(x)
User Profiles
Categ...
 Twitter & Google+ Dataset from about.me
• 429 active users using Google+ and Twitter
 Experiment
• task: recommending 1...
 Results - MRR
10
Aggregated User Interest Profiles
• GmTn : m & n denote the weights for Google+ & Twitter
• Tonly : ent...
 Results - recall
11
Aggregated User Interest Profiles
• GmTn : m & n denote the weights for Google+ & Twitter
• Tonly : ...
 Category-based User Profiles
• T(Cat): replacing entities with the categories from DBpedia
applying the same weights
• T...
 Results - MRR
Interest Propagation
13
 Results - recall
Interest Propagation
14
Conclusions & Future Work
 Conclusions
• a higher weight for the targeted OSN for aggregation
• category-based user profi...
16
Thank you for your attention!
Guangyuan Piao
homepage: http://parklize.github.io
e-mail: guangyuan.piao@insight-centre....
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UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations

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In this paper, we study if reusing Google+ profiles can provide reliable recommendations on Twitter to resolve the cold start problem. Next, we investigate the impact of giving different weights for aggregating user profiles from two OSNs and present that giving a higher weight to the targeted OSN profile for aggregation allows the best performance in the context of a personalized link recommender system. Finally, we propose a user modeling strategy which combines entity-and category-based user profiles using with a discounting strategy. Results show that our proposed strategy improves the quality of user modeling significantly compared to the baseline method.

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UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations

  1. 1. 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
  2. 2. 2 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
  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. 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 concepts for interest propagation • focus on words • assumption: a single doc contains rich information • cannot provide semantic relationships among words
  5. 5.  Bag-of-Concepts 5 dbpedia:The_Black_Keys dbpedia:Eagles_of_Death_Metal Background – User Modeling
  6. 6.  Aggregated User Interest Profiles  Interest Propagation Related Work flickr Category:Indie_rock The_Black_Keys Eagles_of_Death_Metal genre genre 7 times more interests using category-based profiles might be helpful in the context of recommender systems deliciou s stumble upon twitter face book Abel et al. [UMUAI’13] Orlandi et al. [SEMANTiCS’12] same weight for each Online Social Network(OSN) profile
  7. 7.  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 7 Aim of Work
  8. 8. 8 User Modeling Framework propagation strategy using DBpedia 1. T(Cat) 2. T(CatDiscount) 3. Tonly+T(x) User Profiles Category: Smartphones … iPhone 0.12 … 0.08Concept Frequency entity-based user profiles normalization
  9. 9.  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 9 Experiment Setup used for user modeling ground truth recommendation time links (URLs)
  10. 10.  Results - MRR 10 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
  11. 11.  Results - recall 11 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
  12. 12.  Category-based User Profiles • T(Cat): replacing entities with the categories from DBpedia applying 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
  13. 13.  Results - MRR Interest Propagation 13
  14. 14.  Results - recall Interest Propagation 14
  15. 15. 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 15
  16. 16. 16 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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