Usage Meets Link Analysis:
Towards Improving Intranet and Site
Specific Search via Usage Statistics
by Bilgehan Uygar Oztekin
Thesis committee: Vipin Kumar, George Karypis,
Jaideep Srivastava, Gediminas Adomavicius
Dept. of Computer Science, University of Minnesota
200 Union Street SE, Minneapolis, MN 55455
Final Oral Presentation
July 20, 2005
Outline
Aim: Improve ranking quality by incorporating usage
statistics.
Summary of what is and is not addressed in this study.
Ranking methods
Existing link analysis approaches: PageRank and HITS
Usage based modifications/approaches: UPR, UHITS, and Counts
Infrastructure/modules.
Experimental result
Global comparisons/observations
Query dependent comparisons
Discussions
Ranking in search engines
Given a description of what the user is
looking for:
How to select relevant documents?
How to order them?
Typically, a large number of items that
match the criteria.
Coverage/recall tend to be relatively high
without much effort.
Precision/ordering are the primary concerns.
Quality signals come to aid
Modern search engines use various quality
measures.
Some are used to increase recall
• Stemming
• Synonyms/concept indexing
• Query modification (expansion/rewrite).
More effort is spent for better
precision/ordering:
• One of the important milestones:
 Link analysis, in particular, PageRank [Brin98].
• Topic sensitive link analysis 2002.
• What is next? Usage statistics?
Why usage?
Most classical link analysis approaches see the
world from author’s or site administrator’s point
of view.
Algorithms mostly depend on static link structure, and
to a small degree, on the page content.
Usage statistics offer additional, valuable
information, telling the story from the user’s
perspective.
If collected properly, it can be used to estimate
The probability that the average user will go to a
particular page directly (page visit statistics).
The probability that the average user will follow a
particular link (link usage statistics).
Question
Can we improve ranking quality by
employing usage statistics?
How can we design and test usage based
ranking algorithms?
Don’t have the resources, more importantly, the
data to test it on a global scale.
We can test it in our own domain, i.e.
intranet/site specific search.
• Full usage statistics are available.
Internet vs. intranets
Are intranets similar to the Internet? In some ways, yes.
We still have documents and links of similar nature.
Smaller scale.
Major differences of particular interest:
Intranets tend to have poorer connectivity
• Link analysis algorithms are not as effective as in the case of web
search.
Heuristics that work well for link analysis on web search may not
be applicable (e.g. site level aggregation approaches, applying link
analysis on the site level).
Extensive usage statistics for the domain/intranet is available.
There is no incentive for spamming the search results within a
single organization. Aim is to provide the information as best as
possible (mostly cooperation, not competition).
Some of these observations make intranet/site specific
search a prime candidate for usage based signals.
Scope of the project: Algorithms
Developed and implemented a number of usage based
quality measures:
UPR, Usage Aware PageRank [Oztekin03], a novel PageRank
variant incorporating usage statistics.
UHITS, usage augmented HITS (Hypertext Induced Topic Search),
a generalization of [Miller02].
A naïve usage based quality measure, a baseline benchmark.
Developed an improvement to UPR, which makes
spamming more difficult/expensive using a modified
counting scheme.
Implemented two major classical link analysis algorithms:
PageRank
HITS
Scope: Infrastructure
Implemented a simple crawler and took a snapshot
of *.cs.umn.edu domain (20K core URLs with full
usage statistics, 65K total URLs).
Processed 6 months worth of usage logs around
the snapshot.
Built static and usage based site-graphs.
Developed a full fledged site specific search
engine, USearch, http://usearch.cs.umn.edu/, along
with various supporting components to evaluate
the algorithms in a real-life search setting.
Scope: Experiments
Studied the effects of adding various degrees of
usage information.
Sample the parameter space.
Studied the effects of the suggested improvement
(modified counting scheme).
Compared various methods in terms of
Distinguishing power they offer.
The nature of the score distributions they provide.
How they are correlated with other methods.
How well they perform in a query based setting.
• Designed 3 sets of query based experiments:
 two experiments based on blind human evaluations.
 one experiment independent of user judgments.
Items that are not addressed
Test the algorithms on a larger scale
Large intranets (e.g. a few million pages).
Internet.
Test a few aspects that did not occur in our domain.
Spamming.
Uneven usage availability.
• Gracefully converges to PageRank, but studying this on larger scale
would have been interesting.
Conduct implicit user judgment based evaluations which
may offer higher coverage/statistical significance.
Not enough usage base.
Parallel/distributed implementations of UPR or other
methods.
Have ideas/suggestions for reasonable implementations.
PageRank [Brin98]
Based on a random walk model
Random walk user has:
Equal probability to follow each link in a given page
Equal probability to go to each page directly
Under certain conditions, forms a probability
distribution.
Relatively inexpensive.
Stable: In general, changing a subsets of nodes /
links does not affect overall scores dramatically.
It scales well to the Web.
Relatively resistant against spamming.
Usage aware PageRank
PageRank (PR)
1 

 (1  d )   
n 
PR( p )  

PR(i ) 
d




C
(
i
)
i

p


Usage aware PageRank (UPR)

 1  a1
 
 a1  Wdirect ( p )   
 1  d   
 n
 



UPR( p )



 1  a2   

UPR( p )  
C (i )

i p


d




UPR
(
i
)

W
(
i

p
)
link

 a2  
 


Wtotal (i )
i p



d is the damping factor.
n is the total number of pages in the dataset.
a1 controls the usage emphasis in initial weights of pages.
a2 controls the usage emphasis on the links.
C(i) is the number of outgoing links from page i.
Wdirect(p) : estimated probability to go to page p directly.
Wlink(i→p) : weight of the link from page i to page p in the usage graph.
Wtotal(i) : total weight of outgoing links from page i in the usage graph.
UPR implementation
Web logs:
Referrer field is empty: user visited the page directly.
Referrer field is non-empty: user followed a link from
the referrer page to the target page.
Simple approach: Directly use counts to
approximate the probabilities.
Wdirect(p)=count(directly visiting p)/∑icount(directly
visiting i)
Wlink(i→p)=count(link from page i to p is traversed)
Wtotal(i)=total number of times an outgoing link from
page i is traversed.
Wlink(i→p)/Wtotal(i)=estimated probability of following
the link i→p, given that the user follows a link from
page i.
Improvement
Idea: Many people accessing a page/link a total of
x times should count more than few people
accessing the page/link a total of x times. Divide
the dataset into time windows. For each window,
use modified_count=log2(1+count)
Same as counts if everybody accesses the page once.
If the same person (IP number) accesses the page/link
more than once in the same time window, subsequent
contributions are lowered.
Makes the system more robust against usage based
spamming and reduces some undesirable effects (e.g.
browser homepages).
UPR properties
Inherits basic PageRank properties.
Usage information can be updated incrementally
and efficiently.
UPR iterations have similar time complexity as
PageRank iterations (only a very small constant
times more expensive).
Usage importance can be controlled smoothly via
the parameters a1 and a2 (used as sliders, 0=pure
static structure, 1=pure usage).
Can also work with limited usage information (it
gradually converges to PR, as less and less usage
information is available. At the extreme case it
reduces to PR).
HITS [Kleinberg99]
Two scores are assigned to each page: Hub and
authority.
A good authority is a page that is pointed by good
hubs, and a good hub is a page that points to good
authorities.
Not as stable and scalable as PageRank
Mostly used for limited number of documents
(e.g. in the context of focused crawling).
a p   hi
hi 1  A  ai
h p   ai
ai 1  At  hi
i p
p i
UHITS
HITS
UHITS
hi 1  A  ai
Am  (1   )  Astatic    Ausage
ai 1  At  hi
hi 1  Am  ai
ai 1  Amt  hi
a is the authority score vector.
h is the hub score factor.
A is the adjacency matrix.
A(i,j) is nonzero if there is a link from i to j, 0 otherwise.
Am is the modified adjacency matrix.
Astatic (same as A) is the adjacency matrix in static linkage graph.
Ausage is the adjacency matrix in usage based linkage graph.
α controls usage emphasis.
Adjacency matrices are normalized on outgoing links.
UHITS is a generalization of [Miller01]
Naïve approach
Counts: use number of visits to a page as a
direct quality measure for that page.
Score(p)=count(p)/∑count(i)
MCounts: instead of direct counts, use
modified counts (log transform)
Score(p)=mcount(p)/∑mcount(i)
For a given time window, contribution of
subsequent accesses from the same IP number
is reduced.
Sum of scores is 1.
Previous usage based approaches
[Schapira99] or Directhit approach.
A page’s score is proportional to the number of times users clicked the URL of the
page from search engine results (if you record them).
Somewhat similar to naïve approaches considered in this study, but instead of using
global statistics, statistics available to the search engine’s web server are used.
Potential self-reinforcement problem.
[Miller01]: Modification to HITS. Adjacency matrix is replaced by usage
based adjacency matrix (similar to UHITS where parameter α is set to 1).
Unlike UHITS, if a link does not appear within the usage based graph, it is ignored.
Thus scores of many pages may converge to zero.
Practically no experimental results.
[Zhu01]: PageRate, claims to be a modification of PageRank, but has very
different properties.
Does not have the basic PageRank properties.
Normalization is done on incoming links.
Requires non-trivial additional nodes, or normalization steps.
Score of a page is not distributed to the pages it points to, proportional to the
weights of the links.
Unlike UPR, it does not use page visit statistics.
No experimental results provided.
Impact of proposed algorithms
In particular UPR has a number of properties
previous two approaches of incorporating usage
statistics into link analysis did not offer:
Ability to control usage emphasis.
Ability to work with limited usage.
Inherits basic PageRank properties:
•
•
•
•
Stable
Scalable
Normalized scores
Intuitive interpretation
Infrastructure
Experimental Results
Overview
Global comparisons
Effects of usage.
Correlation between various methods.
Distinguishing power different methods offer.
Query dependent comparisons
Compare a representative of each method in a
query dependent setting via blind evaluations.
Design an experiment in which the relevant
documents can be selected objectively without
user feedback (A very specific query type).
Experimental Setup
Methods under comparison:
Counts: use number of visits to pages as a direct quality
measure
MCounts: similar to Counts, but contribution of the
same IP in the same time window is decaying after the
first visit (modified counting scheme).
PR: PageRank (Brin and Page)
UPR: sampled a1 and a2 in 0.25 increments. A total of
25 score vectors computed for UPR (a1=a2=0 is the
same as PR). Damping factor was set to 0.85.
HITS: authorities and hubs (Kleinberg).
UHITS: HITS modified via usage statistics. Usage
emphasis is sampled in 0.25 increments (0 is the same
as HITS). Total of 5 samples from each of the score
vectors (authority, hub).
Effects of usage: PR vs. UPR
Using PR, pages with high connectivity such as
manual pages, FAQs, and discussion groups
dominate top ranks. Department’s main page is at
the 136th position.
As more usage emphasis is introduced, increasing
number of highly used pages (e.g. user home
pages) start to appear in top ranks. For a1=a2=0.25,
department’s main page is at the 6th position, for
values above 0.5, it is the highest ranked page.
Divided PR by UPR(a1=a2=1.0) and sorted the list
in ascending order: 389 out of top 500 pages have
“~” in the URL, compared to 79 out of 500 at the
bottom of the list. UPR boosts highly used pages
including user homepages.
Effects of suggested improvement
UPR simple/UPR modified. Does not affect most URLs.
Effects of suggested improvement
UPR simple/UPR modified. Top and bottom 500 pages.
Effects of suggested improvement
Scores of most pages did not change dramatically.
However, it helped reduce the scores of pages that are
accessed by very few users a large number of times.
www-users.cs.umn.edu/~*userone*/ip
• Likely to be used as a poor man’s dynamic DNS solution: Home
computer’s IP address uploaded periodically, and checked frequently
to obtain the latest IP.
• Ranks with emphasis values 1, 0.75, 0.5:
 UPR simple: 2nd, 6th, 10th
 UPR modified: 130th, 180th, 329th
www-users.cs.umn.edu/~*usertwo*/links.html
• Gateway to various search engines, as well as local and global links.
Used as homepage by a few graduate students, producing a hit every
time they open up a browser.
• Ranks with emphasis values 1, 0.75, 0.5:
 UPR simple: 3rd, 10th, 18th
 UPR modified: 40th, 67th, 116th
PageRank
UPR(0.75)
HITS authority
HITS hub
UHITS(0.75) authority
UHITS(0.75) hub
Counts
Pairwise correlations
Focusing on the internal 20k nodes, for each method pair,
calculate
Pearson correlation (compares scores)
Spearman correlation (compares ordering)
Pearson correlation does not tell the whole story: Score
distribution in link analysis is often exponential. Pearson
correlation effectively measures the correlation between
highly scored pages.
Spearman correlation focuses on the ordering. Seemingly
highly correlated scores may suggest relatively different
rank order:
UPR(0.5, 0.5) vs. Counts has a Pearson correlation of 0.96, the
Spearman correlation is relatively lower, 0.38. It is likely that
different methods suggest different orderings even in top 5
positions.
Comparing scores (Pearson)
Comparing orderings (Spearman)
Observations
PageRank variants offer a global ordering and
behaves smoothly as the parameters are changed.
UPR is more correlated with PR when usage
emphasis is low, and more correlated with
Counts/MCounts when usage emphasis is high.
Counts/MCounts behave reasonably well, but have
a step-like behavior.
HITS variants fail to offer a smooth global
ordering between pages. A significant portion of
scores converge to zero, especially with higher
usage emphasis.
PR vs. UPR(0.25)
UHITS(0.25) auth. vs. UPR(0.25)
MCounts vs. UPR(0.75)
UHITS(0.50) hub vs. Counts
Query dependent evaluations
Evaluation version of USearch
Top 50 documents according to similarity to query are
presented to the user.
• no selection of methods, always uses cosine similarity.
User selects up to 5 relevant documents.
Top 50 results are reranked with methods under
comparison.
Average position of the selected documents are
calculated for each method (lower is better).
Selected methods:
PageRank, UPR(0.75), MCounts, HITS authority/hub,
UHITS(0.75) authority/hub.
Evaluation interface
Query dependent comparisons
3 set of experiments with different characteristics:
Public evaluations (106 queries):
•
•
•
•
•
Everyone can participate. Announced to grads/faculty.
Queries selected by users.
Less accurate results per query.
Queries tend to be more general.
Presentation bias probable.
Arbitrated set (22 queries)
•
•
•
•
•
Queries selected and evaluated by 5 grad students depending on their interests.
More specific queries.
Good quality results are discussed before issuing the query.
Results examined and discussed by at least 2 raters.
Presentation bias is less likely.
Name search (25 queries)
•
•
•
•
•
25 users are randomly selected from CS directory.
Finger utility is used to obtain the username.
Last name is issued as the query.
Average position of the user homepages is calculated for each method.
Presentation bias is not applicable.
Results
Cosine
PR
UPR
Modif.
Counts
HITS
Auth.
HITS
Hub
UHIT
Auth.
UHITS
Hub
Public
8.1
10.1
9.0
10.3
12.9
11.8
11.6
9.9
Arbitrated
10.1
7.6
4.8
5.2
6.4
11.3
7.9
7.8
Name search
3.0
4.8
3.0
3.1
6.7
10.8
8.0
6.4
UPR behaves consistently better than other methods.
MCounts behaves reasonably well.
UHITS fail to offer a distinction between pages in general
except top few documents with very high usage (cosine
order is preserved for the rest).
Cosine order is better than other methods in Public
evaluation set. Potential bias towards presentation order.
Discussions
Applicability to web search
Search engines can also collect general usage
information:
• Specialized browsers/tools/proxies (e.g. toolbars, Google web
accelerator).
There is no reason why UPR can not be computed
on a larger scale:
Leading search engines have the infrastructure to
compute UPR. For Google:
• MapReduce [Dean 2004]: Divide the data set between large
number of machines. Minimal inter-communication
requirements. UPR can be implemented in terms of almost
embarrassingly parallel tasks.
• GFS [Ghemawat 2003]: Transparent, large scale storage.
Discussions (cont)
Is there a reason why web search engines
may be reluctant to use extensive usage
information?
Spamming…
An arms race.
As long as cost of spamming is relatively low
compared to its commercial benefits,
spamming is likely to remain an issue.
Search engines introduce new signals to
increase the cost of spamming.
Discussions (cont)
Early search engines primarily used term
frequency and tf-idf based approaches
Results practically depend on the page alone.
Spammers use keyword stuffing.
Link analysis
Score depends on multiple pages/links.
Spammers use link exchanges/farms (relatively more
expensive, requires modifications to multiple pages).
Usage augmented link analysis
Not only links/pages need to be created/modified, they
need to be supported by a sustained traffic from
multiple sources.
But, if implemented poorly, usage based spamming
may become more cost effective than link based or
other types of spamming.
Discussions (cont)
UPR with modified counts (log transform):
Reduces subsequent contributions of the same
source in a given time window.
Effectively, increases the cost of usage based
spamming.
Alternative approaches are also possible:
Use more aggressive functions.
• E.g. contribution of 0 or 1 per time window.
Cluster or group users, networks, IP ranges etc.,
treat them as a single (or similar) source.
Bottom line
Search engines employ a large number of signals.
They constantly need to check benefits/costs.
Develop new signals.
Maintain existing ones.
Swap in/out signals when dynamics change.
An ideal quality signal:
Has high benefit
• Improve rankings in many situations.
With minimal cost
• Does not harm rankings in other situations (ignoring
computational and other considerations).
Bottom line (cont)
In real life, we settle with good benefit/cost ratio.
For intranet/site specific search, among all quality
measures that are examined, UPR has the best behavior.
• It has desirable properties.
• Visibly improved the ranking quality in various settings.
• Practically did not harm the results in any of the experiments.
Added benefit depends on existing signals.
If existing signals cover usage statistics and/or link
analysis extensively. UPR may not offer the same
impact.
Concluding remarks
Question: Can we improve ranking quality over
existing link analysis approaches in intranet/site
specific search domain?
Experimental results suggest that the answer is
positive:
UPR provided the best quality signal among all
methods examined.
MCounts, a rather simplistic usage based method
performed reasonably well.
There is no incentive for spamming the results in a
single organization.
Unlike web search, relatively simple usage based
approaches can safely be used to improve ranking
quality in intranet/site specific search.
References
[Brin98] S. Brin, and L. Page. “The anatomy of a large-scale
hypertextual Web search engine”. Computer Networks and ISDN
Systems, 30(1–7):107–117, 1998.
[Kleinberg99] J. M. Kleinberg. “Authoritative sources in a hyperlinked
environment”. Journal of the ACM, 46(5):604–632, 1999.
[Schapira99] A. Schapira. “Collaboratively searching the web – an
initial study”. Master’s thesis, 1999.
[Zhu01] J. Zhu, J. Hong, J. G. Hughes. “Pagerate: counting web users’
votes”. In Proceedings of the 12th ACM Conference on Hypertext and
Hypermedia, pages 131–132, Rhus, Denmark, 2001.
[Miller01] J. Miller, G. Rae, and F. Schaefer. “Modifications of
Kleinberg’s HITS Algorithm Using Matrix Exponentiation and
WebLog Records”. In ACM SIGIR Conference posters, September 913, 2001, New Orleans, Louisiana, USA, pages 444–445.
[Dean 2004] J. Dean, and S. Ghemawat. “MapReduce: Simplified Data
Processing on Large Clusters”, OSDI'04: Sixth Symposium on
Operating System Design and Implementation,
San Francisco, CA, December, 2004.
Previous work in search/IR
Mearf (http://mearf.cs.umn.edu)
A meta search engine with expert agreement and content based
reranking, online since 2000.
All modules/libraries are implemented in C++.
Flexible, efficient, scalable, and highly portable.
4 new merging/reranking approaches are compared to 2 existing
methods and the results of a single search engine (Google).
User clicks are logged for a period of one year, and processed to
compare the methods. Average position of clicks metric is
introduced, various bias sources examined.
Publications:
• B. U. Oztekin, G. Karypis, V. Kumar, “Expert agreement and content
based reranking in a meta search environment using Mearf”. In
proceedings of the Eleventh International World Wide Web
Conference, May 7-11, 2002, Honolulu, Hawaii.
• B. U. Oztekin, G. Karypis, V. Kumar, “Average position of user clicks
as an automated and non-intrusive way of evaluating ranking
methods”. UMN, Computer Science and Engineering, TR 03-009
Previous work in search/IR (cont)
UPR, Usage aware PageRank.
Early version of the paper describing UPR has appeared in
WWW2003:
• B. U. Oztekin, L. Ertoz, V. Kumar, and J. Srivastava. “Usage Aware
PageRank”. In WWW12 Poster Proceedings”, May 20-24, 2003,
Budapest, Hungary.
Scout (http://scout.cs.umn.edu)
Another meta search engine, based on Mearf’s core library.
New approaches in search are tried:
• Indexing/clustering interface
• Profile based reranking
• Relevance feedback
Publications:
• S. Kumar, B. U. Oztekin, L. Ertoz, S. Singhal, E-H. Han, and V.
Kumar. “Personalized profile based search interface with ranked and
clustered display”. In International Conference on Intelligent Agents,
Web Technologies and Internet Commerce, July 2001, Las Vegas.
Internet, a brief history
70s – Arpanet, limited number of nodes
1974 TCP specification
80s – Continuous expansion, transition to TCP/IP
1984 – DNS introduced, 1000 hosts.
1987 – 10,000 hosts.
1989 – 100,000 hosts.
Early 90s – Transition to WWW, need for search engines
emerges.
1991 – World Wide Web, Gopher
1992 – 1,000,000 hosts
1993 – Mosaic, first widespread web browser. WWW Wonderer,
first widely acclaimed Web robot.
1994 – Galaxy, first searchable directory. Lycos search engine
indexes 54K documents.
Search engines
Late 90s – Search engine boom
1995 – Infoseek is launched (Feb), Metacrawler
launched (June), Altavista is launched and has instant
popularity (Dec)
1996 – Inktomi founded, Hotbot claims to have the
ability to index 10M pages/day.
1997 – Ask Jeeves, Northern Light
1998 – Open Directory Project. Page and Brin
introduces Google (Sep). MSN Search is launched,
Directhit uses click information in ranking.
1999 – Fast search indexes 200M Web pages.
Algorithms
1995 – Early search engines use term index and frequency
for retrieval and ranking.
Spammers use hidden list of keywords to improve chances of
getting a hit.
1998 PageRank published, Google is in infant stage.
1999 HITS is used for focused crawling and improves
ranking quality of early search engines at the expense of
bandwidth and computation.
2000s Transition to link analysis, major search engines use
link analysis to improve ranking.
Spammers now have to modify more than a single document to
boost the ranking considerably.
2002 Topic sensitive link analysis and ranking.
What is next? Usage statistics?
Infrastructure
Mirroring tool
Obtaining a site mirror.
Indexing.
Snippet extraction.
Building the static linkage graph.
Processing the logs
Building the usage based site graph.
Ranking modules use static and usage based site
graphs to produce various ranking vectors.
Ranking vectors are incorporated in a site specific
search engine (USearch).
UMirror, the mirroring tool
Ability to focus on a domain/site via regular
expression matching.
Ability to select the types of items to be mirrored.
Offers a unique mapping from a given URL to
local copy and vice versa (most existing open
source alternatives do not).
Portable.
UMirror uses WGet to retrieve a single file, but
uses its own queuing, parsing, and matching
modules.
USearch, site specific search engine
Supports boolean queries
Uses cosine similarity to query, and an optional, additional
ranking vector to rerank the documents.
Flexible and modular design. Adding a new site or a new
ranking method is easy.
Stable and robust.
Relatively scalable. It should scale well up to medium size
sites, as well as to higher traffic given enough
resources/bandwidth.
Portable. The search engine can be hosted on many
platforms. The service is accessible using a wide range of
browsers.
USearch is available at http://usearch.cs.umn.edu/
USearch interface
Distribution of UPR scores
Distribution of UPR scores with different parameters in log scale (a1=a2=a).
Modified Counts
Comparing scores (dot product)
Comparing norm of difference
MCounts vs. Counts
HITS authority vs. PR
UHITS(0.75) hub vs. UPR(0.75)
Usage based spamming
Modify page visit statistics
Issue get requests to pages you want to boost without
sending a referrer field.
Modify link usage statistics
Issue get requests simulating an existing link.
Create ghost links
Send requests as if a high ranked page sends users to
your page.
• Wow! a lot of people come to this page from Yahoo!, it must
be an important page!
Solution: make sure to verify the existence of a link
before building the usage based graph or while merging
the two graphs.