Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

SEMANTiCS2016 - Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

958 views

Published on

In this paper, we propose user modeling strategies which
use Concept Frequency - Inverse Document Frequency (CF-
IDF) as a weighting scheme and incorporate either or both
of the dynamics and semantics of user interests. To this end,
we first provide a comparative study on different user modeling strategies considering the dynamics of user interests in
previous literature to present their comparative performance.
In addition, we investigate different types of information (i.e.,
categories, classes and connected entities via various proper-
ties) for entities from DBpedia and the combination of them
for extending user interest profiles. Finally, we build our user
modeling strategies incorporating either or both of the best-
performing methods in each dimension. Results show that
our strategies outperform two baseline strategies significantly
in the context of link recommendations on Twitter.

Published in: Data & Analytics
  • Be the first to comment

SEMANTiCS2016 - Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

  1. 1. 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. 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. 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: 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
  5. 5. Bag-of-Concepts dbpedia:The_Black_Keys dbpedia:Eagles_of_Death_Metal Background – User Modeling dbpedia:The_Wombats
  6. 6. 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)
  7. 7. 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
  8. 8. 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)
  9. 9. Concepts • entities, categories and classes from DBpedia which can be used for representing user interests Definition dbpedia:The_Black_Keys dbc:Rock_music_duos subject yago:BluesRockMusicians type entity category class
  10. 10. 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
  11. 11. 11 User Modeling Framework semantic interest propagation temporal dynamics • category • … • category & property • Ahmed • … • Orlandi User 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
  12. 12. 12 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
  13. 13. 13 Semantic Interest Propagation Core propagation strategies • property-based P: property count in DBpedia graph Combine different semantics
  14. 14. 14 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
  15. 15. 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 15 Experiment Setup used for user modeling ground truth links (URLs) recommendation time
  16. 16. Results 16 Study of Weighting Scheme using CF-IDF improves the performance significantly (<.05)
  17. 17. # of concepts after propagation 17 Semantic Interest Propagation • on average, 224 concepts before extension • 1,865, 1,317 and 1,152 respectively after extension
  18. 18. Recommendation Results 18 Semantic Interest Propagation combining different structures of information improves the performance in the context of link recommendations
  19. 19. 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
  20. 20. 20 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)
  21. 21. Results 21 Compare to State-of-Art outperform baselines best: um(CF-IDF, none, category & property)
  22. 22. Conclusions & Future Work 22 • 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
  23. 23. 23 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

×