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An analysis framework for

search sequences

Qiaozhu Mei, University of Michigan

Kristina Klinkner, Yahoo!

Ravi Kumar, Yahoo! Research

Andrew Tomkins, Google

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AlsoTry

Search sequen

ce

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?

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Nested search sequences

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Goal Goal

Session

Mission Mission Mission…

…Goal

Term block Term block

Query Query Query

…

Click Click Click

Query Query

Click Click

fixation fixation fixation

Query level

Click level

Eye-tracking level

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

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

Categorization of features

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easy

rich

non-sequential

sequential

PersonalizedU

niversal

Local

Base structure

Feature Function

Sequence aggregation

Levels of features and equivalent models

0: Access to nothingrandom guess

1: Local non-sequential(current x)simple classification

2: Local easy (current x & past y’s)

HMM3: Local rich (current x; past x & y’s)

CRF4: Personalized and

universal (aggregated over

sequences)

Results for local prediction

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Local, rich, sequential

Sequential-resistant

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

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