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Using Contextual Information to Understand Searching and Browsing Behavior

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Julia Kiseleva's slides for PhD defense on June 13 2016.

The thesis is available by the following link -- https://www.researchgate.net/publication/303285745_Using_Contextual_Information_to_Understand_Searching_and_Browsing_Behavior

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Using Contextual Information to Understand Searching and Browsing Behavior

  1. 1. Using Contextual Information to Understand Searching and Browsing Behavior Julia Kiseleva Eindhoven University of Technology Eindhoven, The Netherlands, June 2016
  2. 2. Using Contextual Information to Understand Searching and Browsing Behavior
  3. 3. Searching Behavior Want to go to CIKM conference QUERY SERP
  4. 4. Browsing Behavior User Preferences
  5. 5. Using Contextual Information to Understand Searching and Browsing Behavior
  6. 6. Contextual Information Explicit Context Implicit Context
  7. 7. Contextual Information Explicit Context Implicit Context
  8. 8. Contextual Information Explicit Context Implicit Context Contextual Situations (Android Tablet, Weekend) Photo credit: Delwin Steven Campbell via Visualhunt.com / CC BY
  9. 9. Using Contextual Information to Understand Searching and Browsing Behavior
  10. 10. Our Main Research Goal How to use contextual information in order to understand users’ searching and browsing behavior on the web? Improve Online User Experience
  11. 11. Applied Studies Browsing Behavior
  12. 12. Destination Finder Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015 J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
  13. 13. Destination Finder Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015 J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
  14. 14. Destination Finder Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015 J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
  15. 15. Destination Finder Optimized Ranking of Destinations Using Contextual Situations Increased User Engagement (Click Trough Rate +3.7%) Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015 J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
  16. 16. Applied Studies Browsing Behavior
  17. 17. Applied Studies Browsing Behavior Searching Behavior &
  18. 18. Changes in User Satisfaction Want to go to CIKM conference QUERY SERP Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’ J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014 J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
  19. 19. Changes in User Satisfaction QUERY SERP , Dynamic over Time Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’ J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014 J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
  20. 20. Changes in User Satisfaction Time Satisfaction Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’ J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014 J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015 QUERY , SERP
  21. 21. Changes in User Satisfaction Time #Reformulations ~ Satisfaction Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’ J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014 J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015 2013 Oct NovSepAugJul QUERY , SERP
  22. 22. Changes in User Satisfaction Before November 2013 After November 2013 Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’ J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014 J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015 QUERY= ‘flawless’
  23. 23. Changes in User Satisfaction Before November 2013 After November 2013 Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’ J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014 J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015 QUERY= ‘flawless’
  24. 24. Applied Studies Browsing Behavior Searching Behavior & Cortana: “What can I help you do now?”
  25. 25. Q1: how is the weather in Chicago Q2: how is it this weekend Q3: find me hotels Q4: which one of these is the cheapest Q5: which one of these has at least 4 stars Q6: find me directions from the Chicago airport to number one User’s dialogue with Cortana: Task is “Finding a hotel in Chicago” Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’ J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016 J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
  26. 26. Q1: find me a pharmacy nearby Q2: which of these is highly rated Q3: show more information about number 2 Q4: how long will it take me to get there Q5: Thanks User’s dialogue with Cortana: Task is “Finding a pharmacy” Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’ J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016 J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
  27. 27. Cortana: “Here are ten restaurants near you” Cortana: “Here are ten restaurants near you that have good reviews” Cortana: “Getting you direction to the Mayuri Indian Cuisine” User: “show restauran ts near me” User: “show the best ones” User: “show directions to the second one”
  28. 28. Cortana: “Here are ten restaurants near you” Cortana: “Here are ten restaurants near you that have good reviews” Cortana: “Getting you direction to the Mayuri Indian Cuisine” User: “show restauran ts near me” User: “show the best ones” User: “show directions to the second one” No Clicks ???
  29. 29. Cortana: “Here are ten restaurants near you” Cortana: “Here are ten restaurants near you that have good reviews” Cortana: “Getting you direction to the Mayuri Indian Cuisine” User: “show restauran ts near me” User: “show the best ones” User: “show directions to the second one” SAT? SAT? SAT ? Overall SAT? ? SAT? SAT? SAT ?
  30. 30. Acoustic Similarity Phonetic Similarity Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’ J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016 J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
  31. 31. Tracking User Interaction Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’ J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016 J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
  32. 32. 3 seconds 6 seconds 33% of ViewPort 66% of ViewPort ViewPortHeight 2 seconds 20% of ViewPort 1s 4s 0.4s 5.4s+ + = Tracking User Interaction Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’ J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016 J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
  33. 33. Quality of Interaction Model Method Accuracy (%) Average F1 (%) Baseline 70.62 61.38 Interaction Model 80.81* (14.43) 79.08* (28.83) * Statistically significant improvement (p < 0,05 ) Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’ J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016 J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
  34. 34. • Contextual information should be taken into account to understand web and mobile users’ behavior • Analyzing behavioral signals over time is needed to detect changes in user satisfaction with web search • Touch signals are crucial for inferring user satisfaction with intelligent assistants on mobile devices Conclusion

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