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Parts 5 & 6: WWW 2018 tutorial on Understanding User Needs & Tasks

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WWW 2018 tutorial on Understanding User Needs & Tasks
Details: https://task-ir.github.io/Task-based-Search/

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Parts 5 & 6: WWW 2018 tutorial on Understanding User Needs & Tasks

  1. 1. Inferring User Tasks and Needs Rishabh Mehrotra1, Emine Yilmaz2, Ahmed Hassan Awadallah3 1Spotify, London 2University College London 3Microsoft Research
  2. 2. Outline of the Tutorial • Section 1: Introduction • Section 2: Characterizing Tasks • Section 3: Tasks Extraction Algorithms • Section 4: Task based Evaluation • Section 5: Applications
  3. 3. Section 5: Applications – Task based personalization –Task based recommendations –Task Tours –Predicting Task Continuation –Task Completion Dialogue Systems
  4. 4. Task Based Personalization • Users tend to be interested in certain tasks when they use search engines • Represent users in terms of tasks they are interested in • Personalize search results based on that – Query recommendation, re-ranking search results, …
  5. 5. Traditional Approach: Topic Based Personalization • Topics commonly constructed in two ways – Manual topical categories (e.g. ODP) – Topic Modelling Based (e.g. LDA) Topics
  6. 6. Personalization: Topics versus Tasks Topics Tasks Finance, Basketball, Jazz Finance, Basketball, Pop music Basketball, Pop music
  7. 7. Task based Personalization [Mehrotra and Yilmaz, ACM RecSys Posters ’14] – Given N users and M tasks – Construct an NxM user-task association matrix R • Cosine similarity between user profiles and task representations – Find the Nxd user feature matrix U, and Mxd task feature matrix T s.t. R ≈ U TT – Discover the d latent features underlying the interactions between users and tasks
  8. 8. Representing Users in the Task-space • Existing user modeling methods fail to differentiate between users having similar topical interests – User curious about "search engines" and an experienced IR researcher – a stockbroker and a normal investor • The objective is to leverage user's topical interest profiles along with user's task associations. Topics Tasks Finance, Basketball, Jazz Finance, Basketball, Pop music Basketball, Pop music
  9. 9. Coupling Topics and Tasks [Mehrotra and Yilmaz, ACM ICTIR’15]
  10. 10. • Construct a 3-mode tensor to jointly model the user's topical and task preferences: – <users, topics, tasks> • Define each tensor component as: • A user's participation in a certain task gets weighted by her topical affinity. Coupling Topics and Tasks [Mehrotra and Yilmaz, ACM ICTIR’15]
  11. 11. • Tensor decomposition to leverage connections between different users across different topics and different tasks • PARAFAC Tensor Decomposition [Stegeman and Sidiropolous, Linear Algebra and Applications, ‘07] • Ui, Vj, Tk are D-dimensional vectors representing users, topics, and tasks, respectively • Discover the D latent features underlying the interactions between users, topics and tasks Coupling Topics and Tasks [Mehrotra and Yilmaz, ACM ICTIR’15]
  12. 12. Evaluation: Collaborative Query Recommendation • Identify user cohorts based on user preferences • Personalize search results based on recommendations from similar users Number of Similar Users
  13. 13. Section 5: Applications – Task based personalization –Task based recommendations –Task Tours –Predicting Task Continuation –Task Completion Dialogue Systems
  14. 14. • Provide heterogeneous recommendations during users’ browsing process • Define tasks as demand sequences embedded in user browsing sessions Task-based Recommendation on a Web-Scale
  15. 15. • Step 1: Collaborative Task Mining: – extract frequent demand sequences from large scale browser logs – achieved via frequent sequence mining problem • Step 2: Task-based Demand Prediction – predict the upcoming demand of a user given the current browsing session – estimate the probability of each demand d ∈ D being the follow-on demand of the current session • Step 3: Task-based Recommendation – Provide site-level recommendations (based on predicted demands) – Provide link-level recommendations (heterogeneous recommendations based on browsing behavior) Task-based Recommendation on a Web-Scale
  16. 16. Task Tours: Helping Users Tackle Complex Search Tasks Automatically create multi-step task tours: – URL labeling with topical category to identify tasks – construction of the task graph that relates tasks to each other – building of the tours using task graph – identification of triggers Task tours help users: – understand the required steps to complete a task, – find URLs related to the active task – alert users to activities they may have missed Task Tours: Helping Users Tackle Complex Search Tasks, CIKM 2012
  17. 17. Predicting Task Continuation – Understand, characterize and detect tasks which will be continued – Bing logs used to identify intent, topics & search behavior associated with long running tasks – Prediction model using various features Search, Interrupted: Understanding and Predicting Search Task Continuation; SIGIR 2012 Task continuation for broad search intent
  18. 18. Task Completion Dialogue Systems – Reinforcement learning based model – Goal directed conversations – Accesses external knowledge base – Slot filling to form a semantic frame End-to-End Task-Completion Neural Dialogue Systems, arXiv 2017
  19. 19. Section 5: Applications – Task based personalization –Task based recommendations –Task Tours –Predicting Task Continuation –Task Completion Dialogue Systems
  20. 20. Summary - I – Query intent understanding • Classification based (ODP, LDA) • Cluster based (Random walks, reformulations) • Session based techniques – Time based segmentation – Content based segmentation – Hybrid segmentation – Extracting search tasks – Evaluating task extraction algorithms – Applications
  21. 21. – Query intent understanding – Extracting search tasks • Task Extraction – Clustering based approaches – Entity oriented task extraction – Structured SVM based bestlinks structures – LDA topics with Hawkes process • Tasks à Subtasks – dd-CRP with embeddings model – BRT Hierarchical Subtask segmentation – Evaluating task extraction algorithms – Applications Summary - II
  22. 22. – Query intent understanding – Extracting search tasks – Evaluating task extraction algorithms • Gold standard dataset • User study based evaluation • Alternative techniques • TREC Tasks Tracks – Applications Summary - III
  23. 23. –Query intent understanding –Extracting search tasks –Evaluating task extraction algorithms –Applications • Task based user modeling • Related Search suggestions • Task based ecommerce recommendations Summary - IV
  24. 24. Ongoing/Future Work • Task based user satisfaction prediction • Digital assistants – Task understanding – Task completion • Book Uber • Deliver food • Task based recommendations • Beyond search – web tasks
  25. 25. Questions? • Rishabh Mehrotra Research Scientist Spotify, London rishabhm@spotify.com • Emine Yilmaz Associate Professor, UCL Faculty Fellow, The Alan Turing Institute Research Consultant, Microsoft Research emine.yilmaz@ucl.ac.uk • Ahmed Hassan Awadallah Research Lead Microsoft Research, Redmond hassanam@microsoft.com
  26. 26. References [1] Ahmed, White, Pantel, Dumais, and Wang. Supporting complex search tasks. In Proceedings of the ACM CIKM 2014. [2] Baeza-Yates, Hurtado, and Mendoza. Query recommendation using query logs in search engines. In Current Trends in Database Technology-EDBT 2004 Workshops, 2005. [3] D. M. Blei and Griths. The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. Journal of the ACM (JACM) 2010. [4] Blundell and Teh. Bayesian hierarchical community discovery. In NIPS 2013. [5] C. Blundell, Y. W. Teh, and K. A. Heller. Bayesian rose trees. In UAI 2010. [6] B. Carterette, E. Kanoulas, M. Hall, and P. Clough. Overview of the trec 2014 session track. 2013. [7] L. D. Catledge. Characterizing browsing strategies in the world-wide web. Computer Networks and ISDN systems, 1995. [8] Chuang and Chien. Towards automatic generation of query taxonomy: A hierarchical clustering approach. In ICDM 2003. [9] N. Craswell and M. Szummer. Random walks on the click graph. In ACM SIGIR 2007. [10] Donato, Bonchi, and Chi. Do you want to take notes?: identifying missions in yahoo! search pad. In WWW 2010.
  27. 27. [11] D. He. Combining evidence for automatic web session identication. Information Processing & Management, 2002. [12] Heller and Ghahramani. Bayesian hierarchical clustering. In ICML 2005. [13] Hua, Song, and Wang. Identifying users' topical tasks in web search. In ACM WSDM 2013. [14] Jones, Rey, Madani, and Greiner. Generating query substitutions. In WWW 2006. [15] R. Jones and K. L. Klinkner. Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In CIKM 2008. [16] J. H. Kim and Kim. Modeling topic hierarchies with the recursive chinese restaurant process. In CIKM 2012. [17] Kotov, Bennett, White, Dumais, and Teevan. Modeling and analysis of cross-session search tasks. In SIGIR 2011. [18] L. Li and H. Deng. Identifying and labeling search tasks via query-based hawkes processes. In KDD 2014. [19] Y. Li. Relationships among work tasks, search tasks, and interactive information searching behavior. ProQuest, 2008. [20] Y. Li and N. J. Belkin. A faceted approach to conceptualizing tasks in information seeking. Information Processing & Management, 2008. References
  28. 28. [21] H. Liao, Song. Evaluating the effectiveness of search task trails. In WWW 2012. [22] J. Liu and N. J. Belkin. Personalizing information retrieval for multi- session tasks: The roles of task stage and task type. In SIGIR 2010. [23] Lucchese, Orlando, Perego, Silvestri, and Tolomei. Discovering tasks from search engine query logs. ACM Transactions on Information Systems, 2013. [24] C. Lucchese, S. Orlando, R. Perego, F. Silvestri, and G. Tolomei. Identifying task-based sessions in search engine query logs. In WSDM 2011. [25] R. Mehrotra, P. Bhattacharya, and E. Yilmaz. Characterizing users' multi- tasking behavior in web search. In CHIIR 2016. [26] Mei, Zhou, and Church. Query suggestion using hitting time. In ACM CIKM 2008. [27] Q. Mei, H. Fang, and C. Zhai. A study of poisson query generation model for information retrieval. In SIGIR 2007. [28] D. Morris. Searchbar: a search-centric web history for task resumption and information re-nding. In CHI 2008. [29] D. Newman, J. H. Lau, K. Grieser, and T. Baldwin. Automatic evaluation of topic coherence. In NAACL 2010. [30] O'Connor, Krieger, and Ahn. Tweetmotif: Exploratory search and topic summarization for twitter. In ICWSM 2010. References
  29. 29. [31] P. Pecina. Lexical association measures and collocation extraction. Language resources and evaluation, 2010. [32] F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In KDD 2005. [33] Segal, Koller, and Ormoneit. Probabilistic abstraction hierarchies. NIPS 2002. [34] Silverstein and Marais. Analysis of a very large web search engine query log. In SIGIR Forum 1999. [35] A. Singla, R. White, and J. Huang. Studying trailnding algorithms for enhanced web search. In SIGIR 2010. [36] Song, Liu, and Wang. Automatic taxonomy construction from keywords. In Proceedings of the 18th ACM SIGKDD 2012. [37] Spink, Koshman, Park, Field, and Jansen. Multitasking web search on vivisimo. com. In ITCC 2005. [38] P. Vakkari. Task-based information searching. Annual review of information science and technology, 2003. [39] H. Wang, X. Song, R. W. White, and W. Chu. Learning to extract cross- session search tasks. In WWW 2013. [40] White, Bennett, and Dumais. Predicting short-term interests using activity-based search context. In CIKM 2010. References
  30. 30. •Deadline: 30th November 2017 •Notification: 15th December 2017 •Workshop: 9th February 2018 aka.ms/wsdm2018-learnir-workshop

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