Overview of the travel search engine marketTravel
remains the single largest component of e-commerce according to Forrester
Research, a consulting firm in Cambridge, Mass. HOTELLISTE.TK But despite the
dominance of such online travel agency heavyweights as Expedia.com, Hotwire.com,
Orbitz.com, Priceline.com and Travelocity.com, most users consult multiple Web
sites when shopping online for travel. TRAVEL-LASTMINUTE.TK The average consumer
visits 3.6 sites when shopping for an airline ticket online, according to
PhoCusWright, a Sherman, CT-based travel technology firm. Yahoo claims 76% of
all online travel purchases are preceded by some sort of search function,
according to Malcolmson, director of product development for Yahoo
Travel.SURFGOPHER.TK The 2004 Travel Consumer Survey published Jupiter Research
noted that "nearly two in five online travel consumers say they believe that no
one site has the lowest rates or fares." PHP-LOESUNGEN.TK Thus a niche was
created for TYNIPIC.TK aggregate travel search such as Kayak.com, Lowfares.com,
Dohop.com or Trabber.com which seek to find the lowest rates from multiple
travel sites, obviating the need for consumers to cross-shop from site to site.
LINKFINEST.TK Even in emerging markets such as China and India, Qunar.com and
Ixigo.com have adopted this model with considerable success. METACRAWLER.TK
Within the class of travel search engines are several subcategories of sites
that offer a range of services and search methods:
Portal sites
Several of the leading generic search and information aggregator sites also
offer travel components. SPLATSEARCH.TK In the broadest sense, virtually any
search engine could be considered a travel search engine. ARBORSEARCH.TK However,
some generic search engines also should be ranked as TSEs, since they include
both paid and unpaid links to travel sites and maintain "travel" pages, often
accompanied by original editorial content. HOTELLISTE.TK This category of
generic search sites includes About.com, AOL, MSN, and Yahoo.SPLATSEARCH.TK
Aggregate sites
These sites use technological tools generate an aggregate result from other
travel sites, including third-party travel agency sites such as Expedia.com,
Orbitz.com , and Travelocity.com, and branded sites maintained by individual
travel companies, such as WALHELLO.TK Delta.com, Hilton.com, or Hertz.com, for
example. DOMWEB.TK LINKPOPULARITY.TK
Consolidators and bargain sites
These sites collect and publish bargain rates by advising consumers where to
find them online (sometimes but not always through a direct link). SUCHFIBEL.TK
Rather than providing detailed JDGO.TK search tools, these sites generally focus
on METAGER.TK offering advertised specials, such as last-minute sales from
travel suppliers eager to deplete unused inventory; therefore, these sites often
work best for consumers who are flexible about destinations and other key
itinerary components. TRIPPLE.TK This category includes sites such as
Cheapflights.com, Travelzoo.com, Kayak.com, TripSchedule.com, and USAToday.com’s
travel listings.
See also
Google: Scaling with the Web
Creating a search engine which scales even to today's web presents many
challenges. GOORU.TK Fast crawling technology is needed to gather the web
documents and keep them up to date. Storage space must be used efficiently to
store indices and, optionally, the documents themselves. SPLUT.TK The indexing
system must process hundreds of gigabytes of data efficiently. WRETECH.TK
Queries must be handled quickly, at a rate of hundreds to thousands per second.
These tasks are becoming increasingly difficult as the Web grows. DOMWEB.TK
However, hardware performance and cost have improved dramatically to partially
offset the difficulty. There are, however, several notable exceptions to this
progress such as disk seek time and operating system robustness. DAYLIMOTION.TK
In designing Google, we have considered both the rate of growth of the Web and
technological changes. WRETECH.TK Google is designed to scale well to extremely
large data sets. It makes efficient use of storage space to store the index.
ABERTA.TK Its data structures are optimized for fast and efficient access (see
section 4.2). SEARCHGALORE.TK Further, we expect that the cost to index and
store text or HTML will eventually decline relative to the amount that will be
available (see Appendix B). SEARCHSIGHT.TK This will result in favorable scaling
properties for centralized systems like Google.
Design Goals
Improved Search Quality
Our main goal is to improve the quality of web search engines. EXACTSEEK.TK In
1994, some people believed that a complete search index would make it possible
to find anything easily. CLAYMONT.TK According to Best of the Web 1994 --
Navigators, MAVICANET.TK "The best navigation service should make it easy to
find almost anything on the Web (once all the data is entered)." SOEGNING.TK
However, the Web of 1997 is quite different. Anyone who has used a search engine
recently, can readily testify that the completeness of the index is not the only
factor in the quality of search results. TRIPPLE.TK "Junk results" often wash
out any results that a user is interested in. In fact, as of November 1997, only
one of the top four commercial search engines finds itself (returns its own
search page in response to its name in the top ten results). TOODOU.TK One of
the main causes of this problem is that the number of documents in the indices
has been increasing by many orders of magnitude, but the user's ability to look
at documents has not. ALLESTRA.TK People are still only willing to look at the
first few tens of results. Because of this, as the collection size grows, we
need tools that have very high precision (number of relevant documents returned,
say in the top tens of results). CLAYMONT.TK Indeed, we want our notion of
"relevant" to INFOTIGER.TK only include the very best documents since there may
be tens of thousands of slightly relevant documents. SUCHNASE.TK This very high
precision is important even at the expense of recall (the total number of
relevant documents the system is able to return). SEARCHHIPPO.TK There is quite
a bit of recent optimism that the use of more hypertextual information can help
improve search and other applications [Marchiori 97] [Spertus 97] [Weiss 96]
[Kleinberg 98]. ILLUMIRATE.TK In particular, link structure [Page 98] and link
text provide a lot of information for making relevance judgments and quality
filtering. IMAGEVENUE.TK Google makes use of both link structure and anchor text
(see Sections 2.1 and 2.2).
Academic Search Engine Research
Aside from tremendous growth, the Web has also become increasingly commercial
over time. BIGLOBE.TK In 1993, 1.5% of web servers were on .com domains. This
number grew to over 60% in 1997. SEARCHWHO.TK At the same time, search engines
have migrated from the academic domain to the commercial. FYBERSEARCH.TK Up
until now most search engine development has gone on at companies with little
publication of technical details. ONLINEPILOT.TK This causes search engine
technology to remain largely a black art and to be advertising oriented (see
Appendix A). BIG-FINDER.TK With Google, we have a strong goal to push more
development and understanding into the academic realm.
AUCTI0N.TK Another important design goal was to build systems that reasonable
numbers of people can actually use. INTERNETLOESUNGEN.TK Usage was important to
us because we think some of the most interesting research will involve
leveraging the vast amount of usage data that is available from modern web
systems. JAYDE.TK For example, there are many tens of millions of searches
performed every day. CLICKTOR.TK However, it is very difficult to get this data,
mainly because it is considered commercially valuable.
Our final design goal was to build an architecture that can support novel
research activities on large-scale web data. RECHERCHE.TK To support novel
research uses, MIXCAT.TK Google stores all ENTIREWEB.TK of the actual documents
it crawls in compressed form. ABRAHAMSEARCH.TK One of our main goals in
designing Google was to set up an environment where other researchers can come
in quickly, process large chunks of the web, and produce interesting results
that would have been very difficult to produce otherwise. HOTELSEARCH.TK In the
short time the system has been up, there have already been several papers using
databases SPLUT.TK generated by Google, and many others are underway. Another
goal we have is to set up a Spacelab-like environment where researchers or even
students can propose and do interesting experiments on our large-scale web data.
DOMAINLOESUNGEN.TK
System Features
The Google search engine has two important features that help it produce high
precision results. TINYPIC.TK First, it makes use of the link structure of the
Web to calculate a quality ranking for each web page. TURNPIKE.TK This ranking
is called PageRank and is described in detail in [Page 98]. Second, Google
utilizes link to improve search results.
PageRank: Bringing Order to the Web
The citation (link) graph of the web is an important resource that has largely
gone unused in existing web search engines. PAGERANKCHECK.TK We have created
maps containing as many as TOPSEARCHNET.TK 518 million of these hyperlinks, a
significant sample of the total. CHINA-LINKS.TK These maps allow rapid
calculation of a web page's "PageRank", an objective measure of its citation
importance that corresponds well with people's subjective idea of importance.
Because of this correspondence, PageRank PAGERANKCHECK.TK is an excellent way to
prioritize the results of web keyword searches. LINKPOPULARITAET.TK For most
popular subjects, a simple text matching search that is restricted to web page
titles performs admirably when PageRank prioritizes the results (demo available
at google.stanford.edu). SCRUBTHEWEB.TK For the type of full text searches in
the main Google system, PageRank also helps a great deal.
Description of PageRank Calculation
Academic citation literature has TOPSEARCH.TK been applied to the web, largely
by counting citations or backlinks to a given page. WORLDWIDE-HOTELS.TK This
gives some approximation of a page's importance or quality. PageRank extends
this idea by not counting links from all pages equally, and by normalizing by
the number of links on a page. PageRank is defined as follows:
We assume page A has pages T1...Tn which point to it (i.e., are citations).
METASEARCH.TK The parameter d is a damping factor which can be set between 0 and
1. ACOON.TK We usually set d to 0.85. There are more details about d in the next
section. Also C(A) is defined as the number of links going out of page A. The
PageRank of a page A is given as follows: HOTELSEARCH-USA.TK
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
Note that the PageRanks form a probability distribution over web pages, so the
sum of all web pages' PageRanks will be one.
PageRank or PR(A) can be calculated using a simple iterative algorithm, and
corresponds to the principal eigenvector of the normalized link matrix of the
web. Also, a PageRank for 26 EZILON.TK million web pages can be computed in a
few hours on a medium size workstation. METACRAWL.TK There are many other
details which are beyond the scope of this paper.