SEO->Hilltop Algorithm
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Hilltop:
A Search Engine based on Expert
Documents
Krishna Bharat
Compaq, Systems Research Center,
Palo Alto, CA 94301
(Current
Address: Google Inc., 2400 Bayshore
Parkway,
Mountain View, CA 94043)
krishna@google.com
George A. Mihaila
Department of Computer Science
University of Toronto.
georgem@cs.toronto.edu
Abstract:
In response to a query a search
engine returns a ranked list
of documents. If the query is
broad (i.e., it matches many
documents) then the returned
list is usually too long to
view fully. Studies show that
users usually look at only the
top 10 to 20 results. In this
paper, we propose a novel ranking
scheme for broad queries that
places the most authoritative
pages on the query topic at
the top of the ranking. Our
algorithm operates on a special
index of "expert
documents."
These are a subset of the pages
on the WWW identified as directories
of links to non-affiliated sources
on specific topics. Results
are ranked based on the match
between the query and relevant
descriptive text for hyperlinks
on expert pages pointing to
a given result page. We present
a prototype search engine that
implements our ranking scheme
and discuss its performance.
With a relatively small (2.5
million page) expert index,
our algorithm was able to perform
comparably on broad queries
with the best of the mainstream
search engines.
1 Introduction
When searching the WWW broad
queries tend to produce a large
result set. This set is hard
to rank based on content alone,
since the quality and "authoritativeness"
of a page (namely, a measure
of how authoritative the page
is on the subject) cannot be
assessed solely by analyzing
its content. In traditional
information retrieval we make
the assumption that the articles
in the corpus originate from
a reputable source and all words
found in an article were intended
for the reader. These assumptions
do not hold on the WWW since
content is authored by sources
of varying quality and words
are often added indiscriminately
to boost the page's ranking.
For example, some pages are
created to purposefully mislead
search engines, and are known
popularly as "spam"
pages. The most virulent of
spam techniques involves deliberately
returning someone else's popular
page to search engine robots
instead of the actual page,
to steal their traffic. Even
when there is no intention to
mislead search engines, the
WWW tends to be crowded with
information on topics popular
with users. Consequently, for
broad queries keyword matching
seems inadequate.
Prior approaches that have used
content analysis to rank broad
queries on the WWW cannot distinguish
between authoritative and non-authoritative
pages (e.g., they fail to detect
spam pages). Hence the ranking
tends to be poor and search
services have turned to other
sources of information besides
content to rank results. We
next describe some of these
ranking strategies, followed
by our new approach to authoritative
ranking - which we call Hilltop.
1.1
Related Work
Three approaches to improve
the authoritativeness of ranked
results have been taken in the
past:
Ranking Based on Human Classification:
Human editors have been used
by companies such as Yahoo!
and Mining Company to manually
associate a set of categories
and keywords with a subset of
documents on the web. These
are then matched against the
user's query to return valid
matches. The trouble with this
approach is that: (a) it is
slow and can only be applied
to a small number of pages,
and (b) often the keywords and
classifications assigned by
the human judges are inadequate
or incomplete. Given the rate
at which the WWW is growing
and the wide variation in queries
this is not a comprehensive
solution.
Ranking
Based on Usage Information:
Some services such as DirectHit
collect information on: (a)
the queries individual users
submit to search services and
(b) the pages they look at subsequently
and the time spent on each page.
This information is used to
return pages that most users
visit after deploying the given
query. For this technique to
succeed a large amount of data
needs to be collected for each
query. Thus, the potential set
of queries on which this technique
applies is small. Also, this
technique is open to spamming.
Ranking
Based on Connectivity: This
approach involves analyzing
the hyperlinks between pages
on the web on the assumption
that: (a) pages on the topic
link to each other, and (b)
authoritative pages tend to
point to other authoritative
pages.
PageRank
[Page et al 98] is an algorithm
to rank pages based on assumption
b. It computes a query-independent
authority score for every page
on the Web and uses this score
to rank the result set. Since
PageRank is query-independent
it cannot by itself distinguish
between pages that are authoritative
in general and pages that are
authoritative on the query topic.
In particular a web-site that
is authoritative in general
may contain a page that matches
a certain query but is not an
authority on the topic of the
query. In particular, such a
page may not be considered valuable
within the community of users
who author pages on the topic
of the query.
An
alternative to PageRank is Topic
Distillation [Kleinberg 97,
Chakrabarti et al 98, Bharat
et al 98, Chakrabarti et al
99]. Topic distillation first
computes a query specific subgraph
of the WWW. This is done by
including pages on the query
topic in the graph and ignoring
pages not on the topic. Then
the algorithm computes a score
for every page in the subgraph
based on hyperlink connectivity:
every page is given an authority
score. This score is computed
by summing the weights of all
incoming links to the page.
For each such reference, its
weight is computed by evaluating
how good a source of links the
referring page is. Unlike PageRank,
Topic Distillation is only applicable
to broad queries, since it requires
the presence of a community
of pages on the topic.
A
problem with Topic Distillation
is that computing the subgraph
of the WWW which is on the query
topic is hard to do in real-time.
In the ideal case every page
on the WWW that deals with the
query topic would need to be
considered. In practice an approximation
is used. A preliminary ranking
for the query is done with content
analysis. The top ranked result
pages for the query are selected.
This creates a selected set.
Then, some of the pages within
one or two links from the selected
set are also added to the selected
set if they are on the query
topic. This approach can fail
because it is dependent on the
comprehensiveness of the selected
set for success. A highly relevant
and authoritative page may be
omitted from the ranking by
this scheme if it either did
not appear in the initial selected
set, or some of the pages pointing
to it were not added to the
selected set. A "focused
crawling" procedure to
crawl the entire web to find
the complete subgraph on the
query's topic has been proposed
[Chakrabarti et al 99] but this
is too slow for online searching.
Also, the overhead in computing
the full subgraph for the query
is not warranted since users
only care about the top ranked
results.
1.2
Hilltop Algorithm Overview
Our approach is based on the
same assumptions as the other
connectivity algorithms, namely
that the number and quality
of the sources referring to
a page are a good measure of
the page's quality. The key
difference consists in the fact
that we are only considering
"expert" sources -
pages that have been created
with the specific purpose of
directing people towards resources.
In response to a query, we first
compute a list of the most relevant
experts on the query topic.
Then, we identify relevant links
within the selected set of experts,
and follow them to identify
target web pages. The targets
are then ranked according to
the number and relevance of
non-affiliated experts that
point to them. Thus, the score
of a target page reflects the
collective opinion of the best
independent experts on the query
topic. When such a pool of experts
is not available, Hilltop provides
no results. Thus, Hilltop is
tuned for result accuracy and
not query coverage.
Our algorithm consists of two
broad phases:
(i)
Expert Lookup
We
define an expert
page as a page
that is about a certain topic
and has links to many non-affiliated
pages on that topic. Two pages
are non-affiliated conceptually
if they are authored by authors
from non-affiliated organizations.
In a pre-processing step, a
subset of the pages crawled
by a search engine are identified
as experts. In our experiment
we classified 2.5 million of
the 140 million or so pages
in AltaVista's index to be experts.
The pages in this subset are
indexed in a special inverted
index.
Given
an input query, a lookup is
done on the expert-index to
find and rank matching expert
pages. This phase computes the
best expert pages on the query
topic as well as associated
match information.
(ii)
Target Ranking
We
believe a page is an authority
on the query topic if and only
if some of the best experts
on the query topic point to
it. Of course in practice some
expert pages may be experts
on a broader or related topic.
If so, only a subset of the
hyperlinks on the expert page
may be relevant. In such cases
the links being considered have
to be carefully chosen to ensure
that their qualifying text matches
the query. By combining relevant
out-links from many experts
on the query topic we can find
the pages that are most highly
regarded by the community of
pages related to the query topic.
This is the basis of the high
relevance that our algorithm
delivers.
Given
the top ranked matching expert-pages
and associated match information,
we select a subset of the hyperlinks
within the expert-pages. Specifically,
we select links that we know
to have all the query terms
associated with them. This implies
that the link matches the query.
With further connectivity analysis
on the selected links we identify
a subset of their targets as
the top-ranked pages on the
query topic. The targets we
identify are those that are
linked to by at least two non-affiliated
expert pages on the topic. The
targets are ranked by a ranking
score which is computed by combining
the scores of the experts pointing
to the target.
1.3
Roadmap
The rest of the paper is organized
as follows: Section 2 describes
the selection and indexing of
expert documents; Section 3
provides a detailed description
of the ranking scheme used in
query processing; Section 4
presents a user-based evaluation
of our prototype implementation;
and Section 5 concludes the
paper.
2 Expert Documents
Broad subjects are well represented
on the Web and as such are also
likely to have numerous human-generated
lists of resources. There is
value for the individual or
organization that creates resource
lists on specific topics since
this boosts their popularity
and influence within the community
interested in the topic. The
authors of these lists thus
have an incentive to make their
lists as comprehensive and up
to date as possible. We regard
these links as recommendations,
and the pages that contain them,
as experts. The problem is,
how can we distinguish an expert
from other types of pages? In
other words what makes a page
an expert? We felt than an expert
page needs to be objective and
diverse: that is, its recommendations
should be unbiased and point
to numerous non-affiliated pages
on the subject. Therefore, in
order to find the experts, we
needed to detect when two sites
belong to the same or related
organizations.
2.1 Detecting Host Affiliation
We define two hosts as affiliated
if one or both of the following
is true:
They share the same first 3
octets of the IP address.
The rightmost non-generic token
in the hostname is the same.
We consider tokens to be substrings
of the hostname delimited by
"." (period). A suffix
of the hostname is considered
generic if it is a sequence
of tokens that occur in a large
number of distinct hosts. E.g.,
".com" and ".co.uk"
are domain names that occur
in a large number of hosts and
are hence generic suffixes.
Given two hosts, if the generic
suffix in each case is removed
and the subsequent right-most
token is the same, we consider
them to be affiliated.
E.g.,
in comparing "www.ibm.com"
and "ibm.co.mx" we
ignore the generic suffixes
".com" and ".co.mx"
respectively. The resulting
rightmost token is "ibm",
which is the same in both cases.
Hence they are considered to
be affiliated. Optionally, we
could require the generic suffix
to be the same in both cases.
The
affiliation relation is transitive:
if A and B are affiliated and
B and C are affiliated then
we take A and C to be affiliated
even if there is no direct evidence
of the fact. In practice some
non-affiliated hosts may be
classified as affiliated, but
that is acceptable since this
relation is intended to be conservative.
In
a preprocessing step we construct
a host-affiliation lookup. Using
a union-find algorithm we group
hosts, that either share the
same rightmost non-generic suffix
or have an IP address in common,
into sets. Every set is given
a unique identifier (e.g., the
host with the lexicographically
lowest hostname). The host-affiliation
lookup maps every host to its
set identifier or to itself
(when there is no set). This
is used to compare hosts. If
the lookup maps two hosts to
the same value then they are
affiliated; otherwise they are
non-affiliated.
2.2
Selecting the Experts
In this step we process a search
engine's database of pages (we
used AltaVista's crawl from
April 1999) and select a subset
of pages which we consider to
be good sources of links on
specific topics, albeit unknown.
This is done as follows:
Considering
all pages with out-degree greater
than a threshold, k (e.g., k=5)
we test to see if these URLs
point to k distinct non-affiliated
hosts. Every such page is considered
an expert page.
If
a broad classification (such
as Arts, Science, Sports etc.)
is known for every page in the
search engine database then
we can additionally require
that most of the k non-affiliated
URLs discovered in the previous
step point to pages that share
the same broad classification.
This allows us to distinguish
between random collections of
links and resource directories.
Other properties of the page
such as regularity in formatting
can be used as well.
2.3
Indexing the Experts
To locate expert pages that
match user queries we create
an inverted index to map keywords
to experts on which they occur.
In doing so we only index text
contained within "key phrases"
of the expert. A key phrase
is a piece of text that qualifies
one or more URLs in the page.
Every key phrase has a scope
within the document text. URLs
located within the scope of
a phrase are said to be "qualified"
by it. For example, the title,
headings (e.g., text within
a pair of <H1> </H1>
tags) and anchor text within
the expert page are considered
key phrases. The title has a
scope that qualifies all URLs
in the document. A heading's
scope qualifies all URLs until
the next heading of the same
or greater importance. An anchor's
scope only extends over the
URL it is associated with.
The inverted index is organized
as a list of match positions
within experts. Each match position
corresponds to an occurrence
of a certain keyword within
a key phrase of a certain expert
page. All match positions for
a given expert occur in sequence
for a given keyword. At every
match position we also store:
An
identifier to identify the phrase
uniquely within the document
A code to denote the kind of
phrase it is (title, heading
or anchor)
The offset of the word within
the phrase.
In addition, for every expert
we maintain the list of URLs
within it (as indexes into a
global list of URLs) and for
each URL we maintain the identifiers
of the key phrases that qualify
it.
To avoid giving long key phrases
an advantage, the number of
keywords within any key phrase
is limited (e.g., to 32).
3
Query Processing
In response to a user query,
we first determine a list of
N experts that are the most
relevant for that query. E.g.
N = 200 in our experiment. Then,
we rank results by selectively
following the relevant links
from these experts and assigning
an authority score to each such
page. In this section we describe
how the expert and authority
scores are computed.
3.1 Computing the Expert Score
For an expert to be useful in
response to a query, the minimum
requirement is that there is
at least one URL which contains
all the query keywords in the
key phrases that qualify it.
A fast approximation is to require
all query keywords to occur
in the document. Furthermore,
we assign to each candidate
expert a score reflecting the
number and importance of the
key phrases that contain the
query keywords, as well as the
degree to which these phrases
match the query.
Thus, we compute the score of
an expert as as a 3-tuple of
the form (S0, S1, S2). Let k
be the number of terms in the
input query, q. The component
Si of the score is computed
by considering only key phrases
that contain precisely k - i
of the query terms. E.g., S0
is the score computed from phrases
containing all the query terms.
Si
= SUM{key phrases p with k -
i query terms} LevelScore(p)
* FullnessFactor(p, q)
LevelScore(p)
is a score assigned to the phrase
by virtue of the type of phrase
it is. For example, in our implementation
we use a LevelScore of 16 for
title phrases, 6 for headings
and 1 for anchor text. This
is based on the assumption that
the title text is more useful
than the heading text, which
is more useful than an anchor
text match in determining what
the expert page is about.
FullnessFactor(p,
q) is a measure of the number
of terms in p covered by the
terms in q. Let plen be the
length of p. Let m be the number
of terms in p which are not
in q (i.e., surplus terms in
the phrase). Then, FullnessFactor(p,
q) is computed as follows:
If
m <= 2, FullnessFactor(p,
q) = 1
If m > 2, FullnessFactor(p,
q) = 1 - (m - 2) / plen
Our goal is to prefer experts
that match all of the query
keywords over experts that match
all but one of the keywords,
and so on. Hence we rank experts
first by S0. We break ties by
S1 and further ties by S2. The
score of each expert is converted
to a scalar by the weighted
summation of the three components:
Expert_Score = 232 * S0 + 216
* S1 + S2.
3.2 Computing the Target Score
We consider the top N experts
by the ranking from the previous
step (e.g., the top 200) and
examine the pages they point
to. These are called targets.
It is from this set of targets
that we select top ranked documents.
For a target to be considered
it must be pointed to by at
least 2 experts on hosts that
are mutually non-affiliated
and are not affiliated to the
target. For all targets that
qualify we compute a target
score reflecting both the number
and relevance of the experts
pointing to it and the relevance
of the phrases qualifying the
links.
The target score T is computed
in three steps:
For
every expert E that points to
target T we draw a directed
edge (E,T). Consider the following
"qualification" relationship
between key phrases and edges:
The title phrase qualifies all
edges coming out of the expert
A heading qualifies all edges
whose corresponding hyperlinks
occur in the document after
the given heading and before
the next heading of equal or
greater importance.
A hyperlink's anchor text qualifies
the edge corresponding to the
hyperlink.
For
each query keyword w, let occ(w,
T) be the number of distinct
key phrases in E that contain
w and qualify the edge (E,T).
We define an "edge score"
for the edge (E,T) represented
by Edge_Score(E,T), which is
computed thus:
If occ(w, T) is 0 for any query
keyword then the Edge_Score(E,T)
= 0.
Otherwise, Edge_Score(E,T) =
Expert_Score(E) * Sum{query
keywords w} occ(w, T)
We next check for affiliations
between expert pages that point
to the same target. If two affiliated
experts have edges to the same
target T, we then discard one
of the two edges. Specifically,
we discard the edge which has
the lower Edge_Score of the
two.
To compute the Target_Score
of a target we sum the Edge_Scores
of all edges incident on it.
The list of targets is ranked
by Target_Score. Optionally,
this list can be filtered by
testing if the query keywords
are present in the targets.
Optionally, we can match the
query keywords against each
target to compute a Match_Score
using content analysis, and
combine the Target_Score with
the Match_Score before ranking
the targets.
Figure 1. Hilltop Ranking for
the Query: "jobs"
4 Evaluation
In order to evaluate our prototype
search engine, we conducted
two user studies aiming to estimate
the recall and precision. Both
experiments also involved three
other search engines, namely
AltaVista, DirectHit and Google,
for comparison and were done
in August 1999. Note that the
current rankings by these engines
may differ.
4.1 Locating Specific Popular
Targets
For the first experiment we
asked seven volunteers to suggest
the home pages of ten organizations
of their choice (companies,
universities, stores, etc.).
Some of the queries are reproduced
in the table below:
Alpha Phi Omega Best Buy Digital
Disneyland
Dollar Bank Grouplens INRIA
Keebler
Mountain View Public Library
Macy's Minneapolis City Pages
Moscow Aviation Institute
MENSA OCDE ONU Pittsburg Steelers
Pizza Hut Rice University SONY
Safeway
Stanford Shopping Center Trek
Bicycle USTA Vanguard Investments
The
same query was sent to all four
search engines. We assume that
there is exactly one home page
in each case. Every time the
home page was found within the
first ten results, its rank
was recorded. Figure 2 summarizes
the average recall for the ranks
1 to 10 for each of the four
engines: our engine Hilltop
(HT), Google (GG), AltaVista
(AV), and DirectHit (DH). Average
recall at rank k for this experiment
is the probability of finding
the desired home page within
the first k results.
Figure
2. Average Recall vs. Rank
Our
engine performed well on these
queries. Thus, for about 87%
of the queries, Hilltop returned
the desired page as the first
result, comparable with Google
at 80% of the queries, while
DirectHit and AltaVista succeeded
at rank 1 only in 43% and 20%
of the cases, respectively.
As we look at more results,
the average recall increases
to 100% for Google, 97% for
Hilltop, 83% for DirectHit,
and 30% for AltaVista.
4.2
Gathering Relevant Pages
In order to estimate Hilltop's
ability to generate a good first
page of results for broad queries,
we asked our volunteers to think
of broad topics (i.e., topics
for which it is likely that
many good pages exist) and formulate
queries. We collected 25 such
queries, listed below:
Aerosmith Amsterdam backgrounds
chess dictionary
fashion freeware FTP search
Godzilla Grand Theft Auto
greeting cards Jennifer Love
Hewitt Las Vegas Louvre Madonna
MEDLINE MIDI newspapers Paris
people search
real audio software Starr report
tennis UFO
We
then used a script to submit
each query to all four search
engines and collect the top
10 results from each engine,
recording for each result the
URL, the rank, and the engine
that found it. We needed to
determine which of the results
were relevant in an unbiased
manner. For each query we generated
the list of unique URLs in the
union of the results from all
engines. This list was then
presented to a judge in a random
order, without any information
about the ranks of page or their
originating engine. The judge
rated each page for relevance
to the given query on a binary
scale (1 = "good page on
the topic", 0 = "not
relevant or not found").
Then, another script combined
these ratings with the information
about provenance and rank and
computed the average precision
at rank k (for k = 1, 5, and
10). The results are summarized
in Figure 3.
Figure
3. Average Precision at Rank
k
These results indicate that
for broad subjects our engine
returns a large percentage of
highly relevant pages among
the ten best ranked pages, comparable
with Google and DirectHit, and
better than AltaVista. At rank
1 both Hilltop and DirectHit
have an average precision of
0.92. Average precision at 10
for Hilltop was 0.77, roughly
equal to the best search engine,
namely Google, with a precision
of 0.79 at rank 10.
5
Conclusions
We described a new ranking algorithm
for broad queries called Hilltop
and the implementation of a
search engine based on it. Given
a broad query Hilltop generates
a list of target pages which
are likely to be very authoritative
pages on the topic of the query.
This is by virtue of the fact
that they are highly valued
by pages on the WWW which address
the topic of the query. In computing
the usefulness of a target page
from the hyperlinks pointing
to it, we only consider links
originating from pages that
seem to be experts. Experts
in our definition are directories
of links pointing to many non-affiliated
sites. This is an indication
that these pages were created
for the purpose of directing
users to resources, and hence
we regard their opinion as valuable.
Additionally, in computing the
level of relevance, we require
a match between the query and
the text on the expert page
which qualifies the hyperlink
being considered. This ensures
that hyperlinks being considered
are on the query topic. For
further accuracy, we require
that at least 2 non-affiliated
experts point to the returned
page with relevant qualifying
text describing their linkage.
The result of the steps described
above is to generate a listing
of pages that are highly relevant
to the user's query and of high
quality.
Hilltop most resembles the connectivity
techniques, PageRank and Topic
Distillation. Unlike PageRank
our technique is a dynamic one
and considers connectivity in
a graph specifically about the
query topic. Hence, it can evaluate
relevance of content from the
point of view of the community
of authors interested in the
query topic. Unlike Topic Distillation
we enumerate and consider all
good experts on the subject
and correspondingly all good
target pages on the subject.
In order to find the most relevant
experts we use a custom keyword-based
approach, focusing only on the
text that best captures the
domain of expertise (the document
title, section headings and
hyperlink anchor-text). Then,
in following links, we boost
the score of those targets whose
qualifying text best matches
the query. Thus, by combining
content and connectivity analysis,
we are both more comprehensive
and more precise. An important
property is that unlike Topic
Distillation approaches, we
can prove that if a page does
not appear in our output it
lacks the connectivity support
to justify its inclusion. Thus
we are less prone to omit good
pages on the topic, which is
a problem with Topic Distillation
systems. Also, since we use
an index optimized to finding
experts, our implementation
uses less data than Topic Distillation
and is therefore faster.
The
indexing of anchor-text was
first suggested in WWW Worm
[McBryan 94]. In some Topic
Distillation systems such as
Clever [Chakrabarti et al 1998]
and in the Google search engine
[Page et al 98] anchor-text
is considered in evaluating
a link's relevance. We generalize
this to other forms of text
that are seen to "qualify"
a hyperlink at its source, and
include headings and title-text
as well. Also, unlike Topic
Distillation systems, we evaluate
experts on their content match
to the user's query, rather
than on their linkage to good
target pages. This prevents
the scores of "niche experts"
(i.e., experts that point to
new or relative poorly connected
pages) from being driven to
zero, as is often the case in
Topic Distillation algorithms.
In
a blind evaluation we found
that Hilltop delivers a high
level of relevance given broad
queries, and performs comparably
to the best of the commercial
search engines tested.
6
References
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--------------------------------------------------------------------------------
Krishna Bharat is a member of
the research staff at Google
Inc. in Mountain View, California.
Formerly he was at Compaq Computer
Corporation's Systems Research
Center, which is where the research
described here was done. His
research interests include Web
content discovery and retrieval,
user interface issues in Web
search and task automation,
and relevance assessments on
the Web. He received his Ph.D.
in Computer Science from Georgia
Institute of Technology in 1996,
where he worked on tool and
infrastructure support for building
distributed user interface applications.
George Andrei Mihaila is a Ph.D.
student in the Department of
Computer Science at the University
of Toronto. During the summer
of 1999 he was an intern at
Compaq Computer Corporation's
Systems Research Center, which
is where this research was done.
His research interests include
query languages, information
discovery tools, Web-based information
systems and database integration.
He received his M.Sc. in Computer
Science from the University
of Toronto in 1996 with the
thesis WebSQL - an SQL-like
Query Language for the World
Wide Web.
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