An analysis framework for search sequences

1. An analysis framework for search sequences Qiaozhu Mei, University of Michigan Kristina Klinkner, Yahoo! Ravi Kumar, Yahoo! Research Andrew Tomkins, Google

2. mustang Search sequence … ford mustang www.fordvehicles.com/cars/mustang Nova en.wikipedia.org/wiki/Ford_Mustang AlsoTry www.mustang.com

3. Analysis of search sequences At arbitrary levels • Query sequence, click sequence, … Specific tasks • Query classification, session segmentation, mission detection, … Various features • Previous query, number of clicks, duration, … These are usually handled case-by-case Is there a formal framework of search sequence analysis, so that solutions can be generalized, features are reusable, and baselines can be easily constructed?

4. Nested search sequences Session … Mission Mission Mission … Goal Goal Goal … Term block Term block Query level Query Query Query Query Query Click level Click Click Click Click Click Eye-tracking level fixation fixation fixation

5. Search sequence analysis tasks • Classification • x1, x2, …, xN y • eg, whether the session has a commercial intent • Sequence labeling • x1, x2, …, xN y1, y2, …, yN • eg, segment a search sequence into missions and goals • Prediction • x1, x2, …, xN-1  yN • eg, predict if the user would click on the next page • Similarity • f(S1, S2)  R

6. Sample tasks • Algo – (click); if the next click is on a search result • NextPage – (click); if the next click is on next page • NewQuery – (click); if the next click is a new query • TermBlock – (query); if the next query starts with same term • FirstAlgo – (query); if the top search result will be clicked • HasAlgo – (query); if one of the search results will be clicked • Has3Algo – (query); if at least three search results will be clicked • AlsoTry – (query); if AlsoTry will be clicked • Mission – (query); label each query with {new mission, same mission} • Goal – (query); label each query with {new goal, same goal} • Restart – (query); label with {new mission, same mission, old mission} • TransType – (query); {new, lexical, zoom in, pan, zoom out, match, new page} • Nav – (query); classify a query as navigational/informational • IfRestart – (mission); classify a mission as new/old

7. Categorization of features Levels of featuresand equivalent models 0: Access to nothing random guess 1: Local non-sequential (current x) simple classification 2: Local easy (current x & past y’s) HMM 3: Local rich (current x; past x & y’s) CRF 4: Personalized and universal (aggregated over sequences) Feature Function easy rich non-sequential sequential Base structure Local Universal Personalized Sequence aggregation

8. Results for local prediction Local, rich, sequential Sequential-resistant

9. Summary General framework for search sequence analysis Vocabulary to discuss types of features, models, and tasks Straightforward feature re-use across problems Realistic baselines for various instantiations of analysis tasks Simple mechanism to develop baselines for new sequence analysis tasks Improvements can be expected by including per-task features