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Query Suggestion Using Hitting Time. Qiaozhu Mei † , Dengyong Zhou ‡ , Kenneth Church ‡ † University of Illinois at Urbana-Champaign ‡ Microsoft Research, Redmond. Motivating Examples. Sports center. MSG. 1. Difficult for a user to express information need

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Query Suggestion Using Hitting Time

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Query suggestion using hitting time l.jpg

Query Suggestion Using Hitting Time

Qiaozhu Mei †, Dengyong Zhou ‡, Kenneth Church ‡

† University of Illinois at Urbana-Champaign

‡ Microsoft Research, Redmond


Motivating examples l.jpg

Motivating Examples

Sports center

MSG

1. Difficult for a user to express information need

2. Difficult for a Search engine to infer information need

Food Additive

Query Suggestions: Accurate to express the information need;

Easy to infer information need


Motivating examples cont l.jpg

Motivating Examples (Cont.)

Welcome to the hotel california


Motivating examples personalization l.jpg

Motivating Examples: Personalization

MSR

Metropolis Street Racer

Magnetic Stripe Reader

Molten salt reactor

Mars Sample Return

Mountain safety research

Actually Looking for Microsoft Research…


Research questions l.jpg

Research Questions

  • How can we generate query suggestions in a principled way?

  • Can we generate personalized query suggestions using the same method?

  • Can this method be generalized to other search related tasks?


Rest of this talk l.jpg

Rest of This Talk

  • Random Walk, Hitting Time, and Bipartite Graph

  • Generating Query Suggestion

  • Personalized Query Suggestion

  • Experiments

  • Discussion and Summary


Random walk and hitting time l.jpg

Random Walk and Hitting Time

P = 0.3

  • Hitting Time

    • TA: the first time that the random walk is at a vertex in A

  • Mean Hitting Time

    • hiA: expectation of TA given that the walk starts from vertex i

0.3

k

A

i

0.7

P = 0.7

j


Computing hitting time l.jpg

Computing Hitting Time

hiA = 0.7 hjA + 0.3 hkA + 1

h = 0

  • TA: the first time that the random walk is at a vertex in A

0.7

k

A

i

  • hiA: expectation of TA given that the walk starting from vertex i

0.7

Apparently, hiA = 0 for those

j

Iterative Computation


Bipartite graph and hitting time l.jpg

Bipartite Graph and Hitting Time

  • Bipartite Graph:

    • Edges between V1 and V2

    • No edge inside V1 or V2

    • Edges are weighted

    • e.g., V1 = query; V2 = Url

5

5

5

A

A

A

4

4

4

V1

V1

V1

0.4

0.4

0.4

V2

V2

V2

k

0.7

0.7

0.7

7

7

7

1

1

1

i

i

i

w(i, j) = 3

j

j

j

Expected proximity of query i to the query A : hitting time of i  A, hiA

  • convert to a directed graph, even collapse one group


Generate query suggestion l.jpg

Generate Query Suggestion

  • Construct a (kNN) subgraph from the query log data (of a predefined number of queries/urls)

  • Compute transition probabilities p(i  j)

  • Compute hitting time hiA

  • Rank candidate queries using hiA

Query

Url

300

T

www.aa.com

aa

15

www.theaa.com/travelwatch/planner_main.jsp

mexiana

american airline

en.wikipedia.org/wiki/Mexicana


Intuition l.jpg

Intuition

  • Why it works?

    • A url is close to a query if freq(q, url) dominates the number of clicks on this url (most people use q to access url)

    • A query is close to the target query if it is close to many urls that are close to the target query


Personalized query suggestion l.jpg

Personalized Query Suggestion

  • Queries are ambiguous

  • Different user  different information need  different query suggestions

  • Simple approach: build the graph, compute hitting time solely based on the user’s history

  • Data Sparseness

    • E.g., you cannot see a query if you never used it

  • Alternative: modify the bipartite graph instead of rebuilding all


Personalize the bipartite graph l.jpg

Personalize the Bipartite Graph

  • Key: How to compute

    • From w(url, user, query) – Sparse data!

    • Compute a smoothed p(Url | User, Query)

Query

Url

Reweight edges using personalized

Probs.

T

aa

www.aa.com

pseudo query:

P

“aa” + user

www.theaa.com/travelwatch/planner_main.jsp

alcoholics anonymous

en.wikipedia.org/wiki/Alcoholics_Anonymous

Introduce a pseudo (personalized query)

american airline

www.alcoholics-anonymous.org


Personalization with backoff mei and church 08 l.jpg

Personalization with Backoff (Mei and Church 08)

Full personalization: sparse data!

156.111.188.243

156.111.188.*

Personalization with backoff:

156.111.*.*

156.*.*.*

No personalization: lose the opportunity

*.*.*.*

  • We don’t have enough data for everyone!

    • - Backoff to classes of users (e.g., IP)


Experiments l.jpg

Experiments

  • Query Suggestion using Query Logs

    • commercial search engine log (1.5 year)

    • 637 million queries; 585 million urls

    • Query-click bipartite graph

  • Author/keyword suggestion using DBLP

    • titles and authors from DBLP

    • 110k of papers, 580k authors

    • Coauthor graph, keyword graph, author-keyword bipartite graph

  • Baselines: nearest neighbor; personalized pagerank


Result query suggestion l.jpg

Result: Query Suggestion

Query = friends


Result query suggestion ii l.jpg

Result: Query Suggestion (II)

Query = aa

Query = ranknet


Results personalized query suggestion l.jpg

Results: Personalized Query Suggestion

Query = msr


Result author suggestion l.jpg

Result: Author Suggestion

Favor students, especially current students

Query = Jon Kleinberg

(personalized

Pagerank is

similar)

Famous researchers + former students


Result keyword suggestion l.jpg

Result: Keyword Suggestion


Result keyword suggestion for author l.jpg

Result: Keyword Suggestion for Author

Query = Michael I. Jordan

Query = Jiawei Han


Discussions l.jpg

Discussions

  • Hitting time effectively boosts infrequent queries

    • Nearest Neighbor & personalized pagerank favorites frequent queries

  • Fast convergence: a few iterations and a subgraph gets most of the value

  • No parameter to tune

  • Can be generalized to many other tasks (on different graphs)


Ranking on query log graph and search tasks l.jpg

Ranking on Query log Graph and Search Tasks

  • Query  Query: query suggestion

  • Url  Url: finding related pages

    www.cs.jhu.edu/~brill 

    • "research.microsoft.com/users/brill”

  • IP  IP:finding similar users

  • Url  Query: Annotation, Summarization, ads term

  • Query  Url: Search

  • IP, Query  Url: Personalized Search

  • IP, Query  Query: Personalized Query Suggestion

  • Many other opportunities!


  • Summary l.jpg

    Summary

    • Generate query suggestions using hitting time on query-click graph

    • Personalized query suggestion

    • Generalizable to other search tasks

    • Future work:

      • Different types of graphs: e.g., query sessions

      • Combine with other features

      • Large scale evaluation


    Slide25 l.jpg

    Thanks!


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