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ASSOCIATIVE BROWSING

ASSOCIATIVE BROWSING. Evaluating. by Simulation. Jin Y. Kim / W. Bruce Croft / David Smith. What do you remember about your documents?. Registration. James. James. Use search if you recall keywords!. What if keyword search is not enough?. Registration.

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ASSOCIATIVE BROWSING

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  1. ASSOCIATIVE BROWSING Evaluating by Simulation Jin Y. Kim / W. Bruce Croft / David Smith

  2. What do you remember about your documents? Registration James James Use search if you recall keywords!

  3. What if keyword search is not enough? Registration Associative browsing to the rescue!

  4. Probabilistic User Modeling • Query generation model • Term selection from a target document [Kim&Croft09] • State transition model • Use browsing when result looks marginally relevant • Link selection model • Click on browsing suggestions based on perceived relevance

  5. Simulating Interaction using Probabilistic User Model Target Doc : Initial Query : James Registration Search Not Relevant (RankD > 50 ) Marginally Relevant (11 < RankD < 50 ) Click On a Result : 1. Two Dollar Regist… Reformulated Query : Two Dollar Registration Target Doc at Top 10 Target Doc at Top 10 End

  6. A User Model for Link Selection • User’s browsing behavior [Smucker&Allan06] • Fan-out 1~3: the number of clicks per ranked list • BFS vs. DFS : the order in which documents are visited

  7. A User Model for Link Selection • User’s level of knowledge • Random : randomly click on a ranked list • Informed : more likely to click on more relevant item • Oracle : always click on the most relevant item • Relevance estimated using the position of target item

  8. Evaluation Results • Simulated interaction was generated using CS collection • 63,260 known-item finding sessions in total • The Value of Browsing • Browsing was used in 15% of all sessions • Browsing saved 42% of sessions when used • Comparison with User Study Results • Roughly matches in terms of overall usage and success ratio

  9. Evaluation Results • Success Ratio of Browsing More Exploration

  10. Summary Associative Browsing Model Evaluation by Simulation • Simulated evaluation showed very similar statistics to user study in when and how successfully associative browsing is used • Simulated evaluation reveals a subtle interaction between the level of knowledge and the degree of exploration Any Questions? Jin Y. Kim / W. Bruce Croft / David Smith

  11. Simulation of Know-item Finding using Memory Model • Build the model of user’s memory • Model how the memory degrades over time • Generate search and browsing behavior on the model • Query-term selection from the memory model • Use information scent to guide browsing choices [Pirolli, Fu, Chi] • Update the memory model during the interaction • New terms and associations are learned t1 t3 t2 t4 t5 t3

  12. Optional Slides

  13. Evaluation Results • Lengths of Successful Sessions

  14. Summary of Previous Evaluation • User study by DocTrack Game [Kim&Croft11] • Collect public documents in UMass CS department • Build a web interface by which participants can find documents • Department people were asked to join and compete • Limitations • Fixed collection, with a small set of target tasks • Hard to evaluate with varying system parameters • Simulated Evaluation as a Solution • Build a model of user behavior • Generate simulated interaction logs If search accuracy improves by X%, how will it affect user behavior? How would its effectiveness vary for diverse groups of users?

  15. Building the Associative Browsing Model 1. Document Collection 2. Concept Extraction 3. Link Extraction 4. Link Refinement Term Similarity Temporal Similarity Co-occurrence

  16. DocTrack Game

  17. Community Efforts based on the Datasets

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