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Sigir2015 q2c

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Sigir2015 q2c

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SIGIR2015 presentation: From Queries to Cards

The growing accessibility of mobile devices has substantially reformed the way users access information.
While the reactive search by query remains as common as before, recent years
have witnessed the emergence of various proactive systems such as Google Now
and Microsoft Cortana. In these systems, relevant content is presented to users based on their context without a query.
Interestingly, despite the increasing popularity of such services, there is very little known about how users interact with them.

In this paper, we present the first study on user interactions with information cards. We
demonstrate that the usage patterns of these cards vary depending on time and location. We also show
that while overall different topics are clicked by users on proactive and reactive platforms,
the topics of the clicked documents by the same user tend to be consistent cross-platform.
Furthermore, we propose a supervised framework for re-ranking proactive cards based on the user's context and
past history. To train our models, we use the viewport duration and clicks to infer pseudo-relevance labels for the cards. Our results suggest that the quality of card ranking can be significantly improved particularly
when the user's reactive search history is
matched against the proactive data about the cards.

SIGIR2015 presentation: From Queries to Cards

The growing accessibility of mobile devices has substantially reformed the way users access information.
While the reactive search by query remains as common as before, recent years
have witnessed the emergence of various proactive systems such as Google Now
and Microsoft Cortana. In these systems, relevant content is presented to users based on their context without a query.
Interestingly, despite the increasing popularity of such services, there is very little known about how users interact with them.

In this paper, we present the first study on user interactions with information cards. We
demonstrate that the usage patterns of these cards vary depending on time and location. We also show
that while overall different topics are clicked by users on proactive and reactive platforms,
the topics of the clicked documents by the same user tend to be consistent cross-platform.
Furthermore, we propose a supervised framework for re-ranking proactive cards based on the user's context and
past history. To train our models, we use the viewport duration and clicks to infer pseudo-relevance labels for the cards. Our results suggest that the quality of card ranking can be significantly improved particularly
when the user's reactive search history is
matched against the proactive data about the cards.

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Sigir2015 q2c

  1. 1. Milad Shokouhi & Qi Guo From Queries to Cards
  2. 2. Reactive Retrieval
  3. 3. Proactive Retrieval (Zero Query)
  4. 4. Credit: Nick Craswell
  5. 5. Credit: Nick Craswell
  6. 6. Third Party Integration https://www.google.com/landing/now/integrations.html
  7. 7. Re-ranking Proactive Card Recommendations Based on Reactive Search History From Queries to Cards
  8. 8. Clicks & Views
  9. 9. Daily Usage
  10. 10. Hourly Usage
  11. 11. User Returns
  12. 12. Clicked Topics (Per-platform)
  13. 13. Clicked Topics (Cross-platform)
  14. 14. Card Re-ranking (Carré)
  15. 15. Features
  16. 16. Results
  17. 17. Head vs. Tail
  18. 18. Feature Importance
  19. 19. Summary
  20. 20. Thank You!

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