Agglomerative Clustering of a Search Engine Query Log

Doug BeefermanLycos Research GroupLycos, Inc.Waltham, MA USA

Adam BergerSchool of Computer ScienceCarnegie Mellon University

Pittsburgh, PA USA

Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Monday, August 21, 2000

Talk overview

MotivationApproachAlgorithmic detailsSample application: Related

searchesExperimental resultsOther Applications

Motivation

Group together similar Internet entities Search queries {“rare books”, “magazines”, “bookstores”}

URLs {http://www.amazon.com, http://www.bn.com}

Users {Doug Beeferman, Adam Berger}

Applications Recommendation systems Automatic taxonomies Automatic communities

Approach

Exploit search result “click log” data, which bridges all three.

“DB searched for books and then went to bn.com” (DB, books,

bn.com)

DB

Lycos

booksSearch:

Search Results

1. www.bn.com2. www.amazon.com3. www.salon.com

bn.com

1 2 3

Approach

Accumulate click log over time:

Time User Query string Selected URL 1 <DB> <“books”> <http://www.bn.com> 2 <AB> <“magazines”> <http://www.amazon.com> 3 <DB> <“biographies”> <http://www.bn.com> 4 <AB> <“books”> <http://www.amazon.com>

DB searched for “books” and then went to bn.com AB searched for “magazines” and then went to amazon.com DB searched for “biographies” and then went to bn.com AB searched for “books” and then went to amazon.com

Approach

“Factor” data into bipartite graphs

DB

AB

books

magazines

biographies

books

magazines

biographies

bn.com

amazon.com

Approach

Choose two of the three entities, say queries and URLs.

Apply agglomerative clustering algorithm to the nodes in its bipartite graph: Merge the two “most similar” nodes in the left-hand-side Merge the two “most similar” nodes in the right-hand-side Repeat until some termination condition applies. At termination queries are clustered on the left-hand-side,

and URLs are clustered on the right-hand-side.

Distance metric

otherwise ,0

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)()(, 21

21

21

21

qNqNqNqN

qNqNqqD

Similarity of two vertices in graph defined as the number of neighbors in common, normalized by the total number of unique neighbors:

Approach

Agglomerative clustering in action 0 1 2 … n

C

A

B

D

E

F

G

H

3

1

2

4

5

6

7

Approach

Agglomerative clustering in action 0 1 2 … n

C, F

A

B

D

E

G

H

3

1

2

4

5

6

7

Approach

Agglomerative clustering in action 0 1 2 … n

C, F

A

B

D

E

G

H

3, 5

1

2

4

6

7

Approach

Agglomerative clustering in action 0 1 2 … n

C, F

A, D, E, H

B

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2, 6, 3, 5

1

4

7

Algorithm

1. Score all pairs of query nodes according to distance metric D.

2. Merge the two query nodes q1 and q2 for which the distance D(q1, q2) is minimal.

3. Score all pairs of URL nodes according to distance metric D.

4. Merge the two URL nodes u1 and u2 for which the distance D(u1, u2) is minimal.

5. Unless a termination condition applies, go to step 1.

Algorithm: complexity

Naïve implementation requires a distance metric computation per pair of nodes, or (|Q|2 + |U|2) time per iteration.

But only a the neighbors of the affected nodes change during a merge, so the per-iteration cost is proportional to the maximum degree of any node.

Application: Related searches

Lycos.com “related searches” feature:

Baseline algorithm based on past audience overlap of individual query pairs

Data-intensive; poor coverage of rare queries

Application: Related searches

Current approach (Query, Suggestion) pairs must be evidenced explicitly in

data.

Alternate approach using query clusters: Train a set of query clusters using click log data For an input query q, identify its cluster Q Output the top members of Q as suggestions

“Query” “Suggestion”

Application: Related searches

Click log data:

About 500,000 click records from a portion of a day on Lycos.com

Pornographic queries filtered out Queries canonicalized:

• downcased• Whitespace collapsed

No URL canonicalization 243,000 unique queries and 362,000 unique URLs remained Ran agglomerative clustering algorithm for 100,000

iterations

Examples of query clusters

lyricsguitar tabstabssong lyrics irs

irs.govinternal revenue service forms

American airlinesaadvantageaa.com

movieshollywoodhbobad movies

casinoslas vegasonline casinoslas vegas strip

disneyvacationsdisney world tickets

fitnessmusclefitness magazines

1. Baseline 2. Full-replacement: Draw all suggestions for an

input query from its cluster 3. Hybrid: Replace only “weak” suggestions

Impact of experiments measured by “clickthrough rate” of the related searches feature:

Related Searches: Experiments

q

q

q

q

)(simpression

)(clicks

1. Baseline : 1.16% 2. Full-replacement: 1.03% 3. Hybrid: 1.31%

Related Searches: Results

q

q

q

q

)(simpression

)(clicks

Experiment Clickthrough rate

Related Searches: Conclusions

Full-replacement strategy (preferring cluster-derived suggestions) is inferior to baseline

Hybrid strategy is superior to both, overcoming data sparseness problems inherent in baseline query pair model.

Other applications

Cluster URLs with respect to queries Cluster URLs with respect to users Cluster users with respect to URLs, queries

All these treat documents as mere URLs. Such “content-ignorance” has its advantages: Faster: Less data manipulation Handles Web pages that lack text Handles Web pages that are restricted access Handles Web pages that are dynamic