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UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations
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UMAP 2011: Analyzing User Modeling on Twitter for Personalized News Recommendations

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  • large dataset of more than 10 million tweets and 70,000 news articles
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    • 1. Analyzing User Modeling on Twitter for Personalized News Recommendations<br />UMAP, Girona, July 13, 2011<br />Fabian Abel, QiGao, Geert-Jan Houben, Ke Tao<br />Web Information Systems, TU Delft<br />
    • 2. The Social Web<br />Help me to tackle the information overload!<br /> Who is this? What are his <br />personal demands? How <br />can we make him happy?<br />Recommend me news articles that now interest me!<br />Help me to find interesting (social) media!<br />Give me personalized support when I do my online training!<br />Personalize my Web experience!<br />Do not bother me with advertisements that are not interesting for me!<br />
    • 3. What we do: Science and Engineering for the Personal Web<br />domains: news social mediacultural heritage public datae-learning<br />Personalized<br />Recommendations<br />Personalized Search<br />Adaptive Systems <br />Analysis and <br />User Modeling<br />Semantic Enrichment, Linkage and Alignment<br />user/usage data<br />Social Web<br />
    • 4. User Modeling Challenge<br />Personalized News Recommender<br />I want my personalized news recommendations!<br />Profile<br />Analysis and <br />User Modeling<br />?<br />(How) can we infer a Twitter-based user profile that supports the news recommender?<br />Semantic Enrichment, Linkage and Alignment<br />
    • 5. 1. Temporal Constraints<br />time period<br />temporal patterns<br />hashtag-based<br />entity-based<br />topic-based<br />2. Profile Type<br />tweet-based<br />further enrichment<br />3. Semantic Enrichment<br />concept frequency<br />4. Weighting Scheme<br />User Modeling Framework<br />Building Blocks for generating valuable user profiles<br />
    • 6. User Modeling Building Blocks<br />1. Temporal Constraints<br />(a) time period<br />1. Which tweets of the user should be analyzed?<br />?<br />(b) temporal patterns<br />Profile?<br />concept weight<br />end<br />start<br />weekends<br />Morning:<br />Afternoon:<br />Night:<br />time<br />June 27<br />July 4<br />July 11<br />
    • 7. User Modeling Building Blocks<br />1. Temporal Constraints<br />Francesca <br />Schiavone<br />T<br />Sport<br />2. Profile Type<br />Francesca Schiavone won French Open #fo2010<br />Francesca Schiavone French Open<br />?<br />#fo2010<br />Profile?<br />concept weight<br />#<br />hashtag-based<br />entity-based<br />French<br />Open<br />T<br />topic-based<br />#<br />fo2010<br />2. What type of concepts should represent “interests”?<br />time<br />June 27<br />July 4<br />July 11<br />
    • 8. User Modeling Building Blocks<br />1. Temporal Constraints<br />(a) tweet-based<br />Francesca <br />Schiavone<br />2. Profile Type<br />Francesca wins French Open<br />Thirty in women's<br />tennis is primordially<br />old, an age when<br />agility and desire<br />recedes as the …<br />Francesca Schiavone<br />Francesca Schiavone won! http://bit.ly/2f4t7a<br />3. Semantic Enrichment<br />Profile?<br />concept weight<br />French Open<br />Tennis<br />French <br />Open<br />(b) further enrichment<br />Tennis<br />3. Further enrich the semantics of tweets?<br />
    • 9. User Modeling Building Blocks<br />1. Temporal Constraints<br />2. Profile Type<br />?<br />Francesca Schiavone<br />4<br />4. How to weight the concepts?<br />3. Semantic Enrichment<br />Profile?<br /> concept weight<br />3<br />French Open<br />6<br />Tennis<br />Concept frequency<br />4. Weighting Scheme<br />weight(FrancescaSchiavone)<br />weight(French Open)<br />weight(Tennis)<br />time<br />June 27<br />July 4<br />July 11<br />
    • 10. User Modeling Building Blocks<br />1. Temporal Constraints<br />time period<br />temporal patterns<br />hashtag-based<br />entity-based<br />topic-based<br />2. Profile Type<br />tweet-based<br />further enrichment<br />3. Semantic Enrichment<br />concept frequency<br />4. Weighting Scheme<br />
    • 11. 1. Temporal Constraints<br />time period<br />temporal patterns<br />hashtag-based<br />entity-based<br />topic-based<br />2. Profile Type<br />tweet-based<br />further enrichment<br />3. Semantic Enrichment<br />concept frequency<br />4. Weighting Scheme<br />Analysis<br />How do the user modeling building blocks impact the (temporal) characteristics of Twitter-based user profiles?<br />
    • 12. Dataset<br />more than:<br /> 20,000<br />Twitter users<br />2<br />months<br />10,000,000<br />WikiLeaks founder, Julian Assange, under arrest in London<br />tweets<br />75,000<br />news articles<br />time<br />Dec 15<br />Jan 15<br />Nov 15<br />
    • 13. Size of user profiles<br />Profile Type<br />~5% of the users do not make use of hashtags<br />hashtag-based profiles are empty<br />entity-based<br />Entity-based user modeling succeeds for 100% of the users<br />topic-based<br />hashtag-based<br />
    • 14. Semantic Enrichment<br />More distinct topics per profile<br />further enrichment<br />(e.g. exploiting links)<br />further enrichment<br />(e.g. exploiting links)<br />More distinct entities per profile<br />Exploiting external resources allows for significantly richer user profiles (quantitatively)<br />Tweet-based<br />Tweet-based<br />entity-based user profiles<br />topic-based user profiles<br />Impact of Semantic Enrichment<br />
    • 15. User Profiles change over time<br />Temporal Constraints<br />Hashtag-based profiles change stronger than entity-based and topic-based profiles<br />d1-distance:<br />difference between current profile and past profile<br />Example:<br />#<br />old<br />new<br />?<br />music<br />The older the profile the more it differs from the current profile<br />tennis<br />football<br />T<br />
    • 16. Temporal patterns of user profiles<br />Temporal Constraints<br />2<br />1. Weekend profiles differ significantly from weekday profiles<br />2. the difference is stronger than between day and night profiles <br />weekday vs. weekend profiles<br />d1(pweekday, pweekend)<br />day vs. night profiles<br />d1(pday, pnight)<br />topic-based user profiles<br />
    • 17. Observations<br />Semantic enrichment allows for richer user profiles<br />Profiles change over time: fresh profiles seem to better reflect current user demands<br />Temporal patterns: weekend profiles differ significantly form weekday profiles<br />
    • 18. 1. Temporal Constraints<br />time period<br />temporal patterns<br />hashtag-based<br />entity-based<br />topic-based<br />2. Profile Type<br />tweet-based<br />further enrichment<br />3. Semantic Enrichment<br />concept frequency<br />4. Weighting Scheme<br />Evaluation<br />How do the user modeling building blocks impact the quality of Twitter-based profiles for personalized news recommendations?<br />And can we benefit from the findings of the analysis to improve recommendations?<br />
    • 19. Twitter-based Profiles for Personalization<br />Task: Recommending news articles (= tweets with URLs pointing to news articles)<br />Recommender algorithm: cosine similarity between user profile and tweets<br />Ground truth: re-tweets of users<br />Candidate items: news article tweets posted during evaluation period<br />5.5 relevant tweets per user<br />5529 candidate news articles<br />Recommendations = ?<br />P(u)= ?<br />time<br />1 week<br />
    • 20. Profile Type<br />Overview: Performance of User Modeling strategies<br />Topic-based strategy improves S@10 significantly<br />#<br />Entity-based strategy improves the recommendation quality significantly (MRR & S@10)<br />T<br />
    • 21. Impact of Semantic Enrichment<br />Semantic Enrichment<br />T<br />Tweet-based<br />Further enrichment<br />Further semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles!<br />
    • 22. Impact of temporal characteristics<br />Temporal Constraints<br />startcomplete<br />startfresh<br />end<br />Adapting to temporal context helps?<br />Selection of temporal constraints depends on type of user profile. <br /><ul><li>Topic-based profiles: </li></ul>adapting to temporal <br /> context is beneficial<br /><ul><li>Entity-based profiles:</li></ul> long-term profiles <br /> perform better<br />Recommendations = ?<br />yes<br />T<br />time<br />no<br />complete: 2 months<br />fresh: 2 weeks<br />end<br />start<br />yes<br />weekends<br />T<br />Recommendations = ?<br />no<br />time<br />
    • 23. Conclusions and Future Work<br />What we did: Twitter-based User Modeling for Recommending News Articles<br />Analysis: <br />Semantic enrichment results in richer user profiles (quantitative)<br />User interest profiles change over time (hashtag-based stronger than others)<br />Weekend/weekday pattern more significant than day/night pattern<br />Evaluation:<br />Best user modeling strategy: Entity-based > topic-based > hashtag-based <br />Semantic enrichment improves recommendation quality<br />Adapting to temporal context helps for topic-based strategy<br />Future work: for what type of personalization tasks can we exploit what type of Twitter profiles?<br />
    • 24. Thank you!<br />Fabian Abel, QiGao, Geert-Jan Houben, Ke Tao<br />Twitter: @persweb<br />http://persweb.org/ <br />http://u-sem.org/<br />
    • 25. Research Questions<br />What type of user interest profiles can we infer from Twitter activities? <br />Can we exploit Twitter-based profiles for personalizing users’ Social Web experience?<br />Personalized news <br />recommendations<br />in time:<br />interest<br />twitter<br />Good Morning! #tooearly<br />?<br />?<br />I like this http://bit.ly/5d4r2t<br />Why do people now blame Julian Assange?<br />time<br />time<br />Ajax deserves it! #sport<br />
    • 26. Analyzing Twitter-based Profiles for Personalized News Recommendations (in time)<br />News Recommendations in time:<br />Interests:<br />Tennis<br />Football<br />Francesca Schiavone is great!<br />Thirty in women's<br />tennis is primordially<br />old, an age when<br />agility and desire<br />recedes as the<br />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…<br />Ajax gives <br />De Jong<br />a break<br />Ajax manager Frank de<br />Boer announced that…<br />Personalized news recommendations<br />interest<br />interest<br />I like this http://bit.ly/4Gfd2<br />Analysis and <br />User Modeling<br />time<br />time<br />topic:Tennis<br />Semantic Enrichment, Linkage, Alignment<br />dbpedia:Schiavone<br />Nice, thank you!<br />oc:Sports<br />event:FrenchOpen<br />tweets<br />
    • 27. User Modeling Challenge<br />Wednesday, July 13th 2011, 9:10am<br />Personalized news recommender<br />Profile?<br />I want my personalized news recommendations!<br />?<br />(How) can we infer a Twitter-based user profile that supports the news recommender?<br />
    • 28. Bob tweets…<br />Why do people now blame Julian Assange?<br />Ajax deserves it! #sport<br />Good Morning! #tooearly<br />I like this http://bit.ly/5d4r2t<br />time<br />Fr, 6am<br />Fr, 3pm<br />Fr, 8pm<br />Sa, 5pm<br />People publish more than 60 million tweets per day!<br />

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