The document analyzes user modeling approaches for generating Twitter-based user profiles to support personalized news recommendations. It explores different profile types (tweet-based, entity-based, topic-based), the impact of semantic enrichment, and how profiles change over time and according to temporal patterns. An evaluation shows that entity-based profiles combined with semantic enrichment most improve recommendation quality and that adapting topic-based profiles to temporal context also helps performance. Future work is needed to understand what profile types best support different personalization tasks.

1. Analyzing User Modeling on Twitter for Personalized News Recommendations UMAP, Girona, July 13, 2011 Fabian Abel, QiGao, Geert-Jan Houben, Ke Tao Web Information Systems, TU Delft

2. The Social Web Help me to tackle the information overload! Who is this? What are his personal demands? How can we make him happy? Recommend me news articles that now interest me! Help me to find interesting (social) media! Give me personalized support when I do my online training! Personalize my Web experience! Do not bother me with advertisements that are not interesting for me!

3. What we do: Science and Engineering for the Personal Web domains: news social mediacultural heritage public datae-learning Personalized Recommendations Personalized Search Adaptive Systems Analysis and User Modeling Semantic Enrichment, Linkage and Alignment user/usage data Social Web

4. User Modeling Challenge Personalized News Recommender I want my personalized news recommendations! Profile Analysis and User Modeling ? (How) can we infer a Twitter-based user profile that supports the news recommender? Semantic Enrichment, Linkage and Alignment

5. 1. Temporal Constraints time period temporal patterns hashtag-based entity-based topic-based 2. Profile Type tweet-based further enrichment 3. Semantic Enrichment concept frequency 4. Weighting Scheme User Modeling Framework Building Blocks for generating valuable user profiles

6. User Modeling Building Blocks 1. Temporal Constraints (a) time period 1. Which tweets of the user should be analyzed? ? (b) temporal patterns Profile? concept weight end start weekends Morning: Afternoon: Night: time June 27 July 4 July 11

7. User Modeling Building Blocks 1. Temporal Constraints Francesca Schiavone T Sport 2. Profile Type Francesca Schiavone won French Open #fo2010 Francesca Schiavone French Open ? #fo2010 Profile? concept weight # hashtag-based entity-based French Open T topic-based # fo2010 2. What type of concepts should represent “interests”? time June 27 July 4 July 11

8. User Modeling Building Blocks 1. Temporal Constraints (a) tweet-based Francesca Schiavone 2. Profile Type Francesca wins French Open Thirty in women's tennis is primordially old, an age when agility and desire recedes as the … Francesca Schiavone Francesca Schiavone won! http://bit.ly/2f4t7a 3. Semantic Enrichment Profile? concept weight French Open Tennis French Open (b) further enrichment Tennis 3. Further enrich the semantics of tweets?

9. User Modeling Building Blocks 1. Temporal Constraints 2. Profile Type ? Francesca Schiavone 4 4. How to weight the concepts? 3. Semantic Enrichment Profile? concept weight 3 French Open 6 Tennis Concept frequency 4. Weighting Scheme weight(FrancescaSchiavone) weight(French Open) weight(Tennis) time June 27 July 4 July 11

10. User Modeling Building Blocks 1. Temporal Constraints time period temporal patterns hashtag-based entity-based topic-based 2. Profile Type tweet-based further enrichment 3. Semantic Enrichment concept frequency 4. Weighting Scheme

11. 1. Temporal Constraints time period temporal patterns hashtag-based entity-based topic-based 2. Profile Type tweet-based further enrichment 3. Semantic Enrichment concept frequency 4. Weighting Scheme Analysis How do the user modeling building blocks impact the (temporal) characteristics of Twitter-based user profiles?

12. Dataset more than: 20,000 Twitter users 2 months 10,000,000 WikiLeaks founder, Julian Assange, under arrest in London tweets 75,000 news articles time Dec 15 Jan 15 Nov 15

13. Size of user profiles Profile Type ~5% of the users do not make use of hashtags hashtag-based profiles are empty entity-based Entity-based user modeling succeeds for 100% of the users topic-based hashtag-based

14. Semantic Enrichment More distinct topics per profile further enrichment (e.g. exploiting links) further enrichment (e.g. exploiting links) More distinct entities per profile Exploiting external resources allows for significantly richer user profiles (quantitatively) Tweet-based Tweet-based entity-based user profiles topic-based user profiles Impact of Semantic Enrichment

15. User Profiles change over time Temporal Constraints Hashtag-based profiles change stronger than entity-based and topic-based profiles d1-distance: difference between current profile and past profile Example: # old new ? music The older the profile the more it differs from the current profile tennis football T

16. Temporal patterns of user profiles Temporal Constraints 2 1. Weekend profiles differ significantly from weekday profiles 2. the difference is stronger than between day and night profiles weekday vs. weekend profiles d1(pweekday, pweekend) day vs. night profiles d1(pday, pnight) topic-based user profiles

17. Observations Semantic enrichment allows for richer user profiles Profiles change over time: fresh profiles seem to better reflect current user demands Temporal patterns: weekend profiles differ significantly form weekday profiles

18. 1. Temporal Constraints time period temporal patterns hashtag-based entity-based topic-based 2. Profile Type tweet-based further enrichment 3. Semantic Enrichment concept frequency 4. Weighting Scheme Evaluation How do the user modeling building blocks impact the quality of Twitter-based profiles for personalized news recommendations? And can we benefit from the findings of the analysis to improve recommendations?

19. Twitter-based Profiles for Personalization Task: Recommending news articles (= tweets with URLs pointing to news articles) Recommender algorithm: cosine similarity between user profile and tweets Ground truth: re-tweets of users Candidate items: news article tweets posted during evaluation period 5.5 relevant tweets per user 5529 candidate news articles Recommendations = ? P(u)= ? time 1 week

20. Profile Type Overview: Performance of User Modeling strategies Topic-based strategy improves S@10 significantly # Entity-based strategy improves the recommendation quality significantly (MRR & S@10) T

21. Impact of Semantic Enrichment Semantic Enrichment T Tweet-based Further enrichment Further semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles!

23. Conclusions and Future Work What we did: Twitter-based User Modeling for Recommending News Articles Analysis: Semantic enrichment results in richer user profiles (quantitative) User interest profiles change over time (hashtag-based stronger than others) Weekend/weekday pattern more significant than day/night pattern Evaluation: Best user modeling strategy: Entity-based > topic-based > hashtag-based Semantic enrichment improves recommendation quality Adapting to temporal context helps for topic-based strategy Future work: for what type of personalization tasks can we exploit what type of Twitter profiles?

24. Thank you! Fabian Abel, QiGao, Geert-Jan Houben, Ke Tao Twitter: @persweb http://persweb.org/ http://u-sem.org/

25. Research Questions What type of user interest profiles can we infer from Twitter activities? Can we exploit Twitter-based profiles for personalizing users’ Social Web experience? Personalized news recommendations in time: interest twitter Good Morning! #tooearly ? ? I like this http://bit.ly/5d4r2t Why do people now blame Julian Assange? time time Ajax deserves it! #sport

26. Analyzing Twitter-based Profiles for Personalized News Recommendations (in time) News Recommendations in time: Interests: Tennis Football Francesca Schiavone is great! Thirty in women's tennis is primordially old, an age when agility and desire recedes as the next wave of younger/faster/stronger players encroaches. It's uncommon for any athlete to have a breakthrough season at 30, but it's exceedingly… Ajax gives De Jong a break Ajax manager Frank de Boer announced that… Personalized news recommendations interest interest I like this http://bit.ly/4Gfd2 Analysis and User Modeling time time topic:Tennis Semantic Enrichment, Linkage, Alignment dbpedia:Schiavone Nice, thank you! oc:Sports event:FrenchOpen tweets

27. User Modeling Challenge Wednesday, July 13th 2011, 9:10am Personalized news recommender Profile? I want my personalized news recommendations! ? (How) can we infer a Twitter-based user profile that supports the news recommender?

28. Bob tweets… Why do people now blame Julian Assange? Ajax deserves it! #sport Good Morning! #tooearly I like this http://bit.ly/5d4r2t time Fr, 6am Fr, 3pm Fr, 8pm Sa, 5pm People publish more than 60 million tweets per day!