System and method for correcting spelling errors in search queries

A search engine is disclosed that uses correlations between search terms to correct misspelled terms within search queries. The correlations are based at least in-part on historical query submissions to the search engine. Preferably, the correlations reflect the frequencies with which the search terms have historically appeared together within the same query, and are stored within a correlation table using related terms lists. In one embodiment, the correlation table is generated periodically from the M (e.g. 10) most recent days of entries in a query log, and thus reflects the current preferences of users. In operation, when a query that includes both matching and non-matching search terms is submitted to the search engine, a spelling correction process accesses the correlation table to generate a list of terms that are deemed to be related to the matching term(s). The spellings of these related terms are then compared to the spelling of each non-matching term using a spelling comparison function that compares two character strings and generates a similarity score. If a suitable replacement is found for a given non-matching term, the non-matching term is replaced with the similar related term. The modified query is then used to perform the search, and the user is notified of the modification(s) made to the query. In the disclosed embodiment, the search engine is used on the Web site of an online merchant to assist users in locating book titles, music titles, and other types of products.

APPENDIX AND COPYRIGHT MATERIALS 
This specification includes as an appendix a C++ listing of a spelling 
comparison function used to compare two character strings. The contents of 
the appendix are subject to copyright protection. The copyright owner has 
no objection to the facsimile reproduction of the patent document or 
portions thereof as it appears in the files or records of the U.S. Patent 
and Trademark Office, but otherwise reserves all rights whatsoever. 
FIELD OF THE INVENTION 
The present invention relates to information searching and retrieval, and 
more specifically, relates to methods for processing search queries. 
BACKGROUND OF THE INVENTION 
Many World Wide Web sites and online services provide search engine 
programs ("search engines") for assisting users in locating items of 
interest from a domain of items. For example, Web sites such as 
AltaVista.TM. and Infoseek.TM. provide search engines for assisting users 
in locating other Web sites, and online services such as Lexis.TM. and 
Westlaw.TM. implement search engines for assisting users in locating 
articles and court opinions. In addition, online merchants commonly 
provide search engines for assisting customers in locating items from an 
online catalog. 
To perform a search using a search engine, a user submits a query 
containing one or more search terms. The query may also explicitly or 
implicitly identify a record field to be searched, such as the title, 
author or subject classification of the item. For example, a user of an 
online bookstore site may submit a query containing terms that the user 
believes appear in the title of a book. A query server program of the 
search engine processes the query to identify any items that match the 
query. The set of items identified by the query server program is referred 
to as the "query result," and is commonly presented to the user as a list 
of the located items. In the bookstore example, the query result would 
typically be the set of book titles that include all of the search terms, 
and would commonly be presented to the user as a hypertextual listing of 
these items. 
When the user of a search engine misspells a search term within a query, 
such as by mistyping or failing to remember the term, the misspelled term 
commonly will not match any of the database terms that are encompassed by 
the search. In this event, many search engines will simply return a null 
(empty) search result. Presenting null search results to users, however, 
can cause significant user frustration. To reduce this problem, some 
search engines effectively ignore the non-matching term(s) during the 
search. This strategy has the disadvantage of failing to take into account 
potentially important information specified by the user, and tends to 
produce query results that contain relatively large numbers of irrelevant 
items. 
SUMMARY OF THE INVENTION 
The present invention addresses the foregoing problems by providing a 
system and method for correcting misspelled terms within search queries. 
The system includes a database of correlation data that indicates 
correlations between search terms. The correlation data is preferably 
based on the frequencies with which specific search terms have 
historically appeared together within the same query, and is preferably 
generated from a query log file. In one embodiment, each entry within the 
database (implemented as a table) comprises a keyword and a "related 
terms" list, wherein the related terms list is composed of the terms that 
have appeared in combination with the keyword with the highest degree of 
frequency. 
The spelling correction method is preferably invoked when a search query is 
submitted that includes at least one matching term and a least one 
non-matching term. Using the correlation database, a list of terms that 
are deemed to be related to the matching term or terms is initially 
generated. This may be accomplished, for example, by extracting the 
related terms list for each matching term, and if the query includes 
multiple matching terms, combining these lists into a single related terms 
list. 
The related terms are then compared in spelling to the non-matching term(s) 
to identify any suitable replacements. The spelling comparisons are 
preferably performed using an anagram-type spelling comparison function 
which generates a score that indicates the degree of similarity between 
two character strings. If a related term with a sufficiently similar 
spelling to a non-matching term is found, the non-matching term is 
preferably automatically replaced with the related term. The user may 
alternatively be prompted to select the replacement term(s) from a list of 
terms. Once the non-matching term or terms have been replaced, the 
modified query is used to perform the search. The user is also preferably 
notified of the modification(s) made to the query. 
An important benefit of the above-described spelling correction method over 
conventional spelling correction methods is that the selected replacement 
terms are considerably more likely to be the terms that were intended by 
the user. This benefit results from the above-described use of search term 
correlation data, and particularly correlation data that reflects 
historical query submissions. The method thereby increases the likelihood 
that the query result will contain items that are of interest to the user. 
Another benefit is that the method is well suited for correcting terms 
that do not appear in the dictionary, such as proper names of authors and 
artists and fanciful terms within titles and product names. 
In accordance with another aspect of the invention, the correlation data is 
preferably generated such that it heavily reflects recent query 
submissions, and thus strongly reflects the current preferences of users. 
This may be accomplished, for example, by periodically generating a 
correlation table from a desired number (e.g., 12) of the most recent 
daily query logs. Using correlation data that heavily reflects recent 
query submissions further increases the likelihood that replacements made 
by the spelling correction process will be those intended by users.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
The present invention provides a method for correcting spelling errors in 
queries that are submitted to search engines. Briefly, the method involves 
using search term correlation data to identify search terms that are 
related to the correctly spelled search term(s) of the query, and 
evaluating whether any of these related terms has a similar spelling to 
the misspelled search term(s). The search term correlation data is 
preferably based on historical query submissions, and more specifically, 
on the frequencies with which search terms have previously occurred 
together within the same query. The method may be implemented within any 
of a variety of different types of search engines, including, for example, 
Internet search engines, legal research search engines, and search engines 
provided by online merchants. 
For purposes of illustration, the method is described herein in the context 
of a search engine that is used to assist customers of Amazon.com Inc. in 
locating items (books, CDs, etc.) from an online catalog of products. 
Throughout the description, reference will be made to various 
implementation-specific details of the Amazon.com implementation. These 
details are provided in order to fully illustrate a preferred embodiment 
of the invention, and not to limit the scope of the invention. The scope 
of the invention is set forth in the appended claims. 
I. Overview of Web Site and Search Engine 
FIG. 1 illustrates the Amazon.com Web site 30, including components used to 
implement a search engine in accordance with the invention. As is well 
known in the art of Internet commerce, the Amazon.com Web site includes 
functionality for allowing users to search, browse, and make purchases 
from an online catalog of book titles, music titles, and other types of 
items. Because the catalog contains millions of browsable items, it is 
important that the site provide an efficient mechanism for assisting users 
in locating items. 
As illustrated by FIG. 1, the Web site 30 includes a Web server application 
32 ("Web server") which processes requests received over the Internet from 
user computers 34. These requests include search queries that are 
submitted by users to search the catalog for products. The Web server 32 
records user transactions, including query submissions, within a query log 
36. In the embodiment depicted in FIG. 1, the query log 36 consists of a 
sequence of daily query log files 36, each of which represents one day of 
transactions. 
The Web site 30 also includes query server 38 which processes the search 
queries by searching a bibliographic database 40. The bibliographic 
database 40 includes information about the various items that are 
available for purchase from the site. This information includes, for 
example, the titles, authors, publishers, subject descriptions and ISBNs 
(International Standard Book Numbers) of book titles, and the titles, 
artists, labels, and music classifications of music titles. The 
information for each item is arranged within fields (such as an "author" 
field and a "title" field), enabling the database 40 to be searched on a 
field-restricted basis. The site also includes a database 41 of HTML 
(Hypertext Markup Language) content which includes, among other things, 
product information pages which show and describe the various catalog 
items. 
FIG. 2 illustrates the general format of a book search page that can be 
used to search the bibliographic database 40 for book titles. The page 
includes author, title and subject fields 42, 43, 44 and associated 
controls that allow the user to initiate field-restricted searches for 
book titles. Users can also access a music search page (not shown) to 
search for music titles using the artist, title and label fields. Other 
areas of the site allow the user to submit search queries without limiting 
the search terms to specific database fields. 
When the user submits a search query from the book search page or another 
page of the site, the query server 38 (FIG. 1) applies the query to the 
bibliographic database 40, taking into account any field restrictions 
within the query. If the query result is a single item, the item's product 
information page is presented to the user. If the query result includes 
multiple items, a list of the items is presented to the user through one 
or more search results pages (not shown) which include hypertextual links 
to the items' respective product information pages. 
For multiple-term queries, the query server 38 effectively logically ANDs 
the search terms together to perform the search. For example, if the user 
enters the terms "Java" and "programming" into the title field, the query 
server 38 will search for and return a listing of all items that have both 
of these terms within the title. Thus, if any search term of the query 
does not produce a match (referred to herein as a "non-matching term"), 
the query will produce a null query result. In this event, the user may be 
presented with a listing of items that are deemed to be "close matches." 
Although the search engine described herein logically ANDs the search terms 
together, it will be recognized that the invention can be applied to 
search engines that use other methods for combining search terms. 
II. Overview of Spelling Correction Feature 
In accordance with the invention, when a user submits a multiple-term 
search query that includes both matching and non-matching terms, a spell 
correction process 48 (FIG. 1) of the query server 38 uses the matching 
term(s), in combination with search term correlation data, to attempt to 
correct the spelling of the non-matching term(s). This is preferably 
accomplished by using a search term correlation table 50 to identify 
additional terms that are deemed to be related to the matching search 
term(s), and then comparing the spellings of such related terms to the 
spelling(s) of the non-matching term(s). For example, if a user submits 
the query 
Java APPI, 
and "APPI" is a non-matching term, the spell correction process 48 uses the 
correlation table to identify a list of terms that are deemed to be 
related to "Java." This list might be as follows: coffee, programming, 
API, management, language. Upon comparing the spellings of these related 
terms to the spelling of the non-matching term, the spell correction 
process 48 will identify "API" as a candidate replacement term. The 
spelling comparisons are preferably performed using an anagram-type 
spelling comparison function which generates a score that indicates the 
degree of similarity between two character strings. 
The data contained within the correlation table 50 indicates relationships 
between search terms, and is used to effectively predict search terms that 
are likely to appear within the same query. Incorporating such predictions 
into the spelling correction process significantly increases the 
likelihood that a given replacement term identified by the process will be 
the term that was intended by the user. 
If the above process identifies a suitable replacement for a given 
non-matching term, the non-matching term is automatically replaced with 
the related term. If no suitable replacement is found for a given 
non-matching term, the non-matching term is preferably deleted from the 
query. Once all of the non-matching terms have either been replaced or 
deleted, the modified query is used to search the bibliographic database 
40 and the result is presented to the user. The user is also notified of 
the modification(s) made to the search query. 
As an alternative to automatically replacing the non-matching term(s), the 
user may be prompted to select the replacement term(s) from a list of 
candidate replacement terms. This is preferably accomplished by presenting 
each candidate replacement term as a respective hyperlink (not shown) 
which can be selected by the user to initiate the modified search; the 
user can thus both select a modified query and initiate a new search with 
a single action. 
In accordance with another aspect of the invention, the correlation table 
50 preferably contains or reflects historical information about the 
frequencies with which specific search terms have appeared together within 
the same search query. As depicted in FIG. 1, this data is preferably 
extracted from the query log 36 using a table generation process 46. 
Incorporating such historical information into the spell correction 
process further increases the likelihood that a replacement term located 
by the process will be the term that was intended by the user. 
In one embodiment (described below), the table generation process 46 
regenerates the correlation table 50 on a daily basis from the M most 
recent daily log files 36(1)-36(M), where M is a fixed number such as ten 
or twenty. This "sliding window" approach advantageously produces a 
correlation table that is based only on recent query submissions, and 
which thus reflects the current preferences of users. For example, if a 
relatively large number of users have searched for the book Into Thin Air 
by Jon Krakauer over the past week, the correlations between the terms 
"into," "thin," "air," and "Krakauer" will likely be correspondingly high; 
these high correlations will in-turn increase the likelihood that a user 
who misspells a term (of a multiple-term query) while searching for this 
book will be directed to the book. Any of a variety of other types of 
biasing methods can be applied to the log to achieve a similar result, 
including other methods which apply a greater weight to recent query 
submissions over aged query submissions. 
FIG. 3 illustrates the general format of the correlation table 50. In the 
embodiment depicted in FIG. 3 and described in detail herein, the 
correlations between search terms are based solely on frequency of 
occurrence within the same query. As described below, other types of 
search term correlations can additionally or alternatively be used. In 
addition, although the disclosed implementation uses a table to store the 
correlation data, other types of databases can be used. 
As illustrated by FIG. 3, each entry within the correlation table (3 
entries shown) preferably has two primary components: (1) a keyword 60, 
and (2) a "related terms" list 62 for that keyword. The related terms list 
62 is a list of the N (e.g. 20) search terms that have appeared within the 
same query as the keyword with the highest degree of frequency, and is 
ordered according to frequency. For example, the entry for the keyword 
COSMOS is: 
COSMOS: ASTRONOMY, SAGAN, UNIVERSE, SE, CARL . . . 
indicating that ASTRONOMY has appeared together with COSMOS with the 
highest degree of frequency; SAGAN has appeared with COSMOS with the 
second highest degree of frequency, and so on. Each term that appears 
within the list portion 62 is deemed to be related to the corresponding 
keyword 60 by virtue of the relatively high frequency with which the terms 
have occurred within the same query. In the implementation described 
herein, the keywords and related terms are stored in the table without 
regard to alphabetic case, although case information can alternatively be 
preserved. 
With further reference to FIG. 3, each related term and each keyword 60 
within the table 50 preferably includes a single-character field prefix 
(not shown) which indicates the search field 42, 43, 44 to which the term 
corresponds (based on the search fields in which the terms were entered by 
users). These prefixes may, for example, be as follows: A=author, T=title, 
S=subject, R=artist, L=label. Thus, for example, if the keyword COSMOS in 
FIG. 3 has the prefix "T" and the related term SAGAN has the prefix "A," 
this would indicate that a relatively large number of queries were 
submitted which included COSMOS in the title field 43 together with SAGAN 
in the author field 42. As described below, the prefixes of the related 
terms are used by the spelling correction process 48 to effectively filter 
out the non-field-corresponding terms from the related terms lists, so 
that a non-matching term within a given search field will only be compared 
to related terms of the same field. Thus, for example, a non-matching term 
within the author field 42 will be compared only to other terms that have 
historically been entered into the author field 42. The spell correction 
process 48 similarly uses the prefixes of the keywords 60 to select table 
entries that correspond to the respective search fields of the matching 
terms. For example, if an erroneous query is received which includes the 
matching term MOUNTAIN within the title field 43, the spelling correction 
process 48 will search for a table entry having the keyword TMOUNTAIN. 
As further depicted in FIG. 3, the correlation table 50 also preferably 
includes correlation scores 64 that indicate the number of times each 
related term has appeared in combination with the keyword. For example, 
term PROGRAMMING has a score of 320 in the entry for JAVA, indicating that 
JAVA and PROGRAMMING appeared within the same query (within the fields 
indicated by their respective field prefixes) three hundred and twenty 
times. As described below, queries that produced a null query result are 
ignored by the table generation process 46, and thus are not reflected in 
the correlation scores 64. In other implementations, the correlation 
scores 64 may incorporate other types of correlations. As described below, 
the scores 64 are preferably used to merge related terms lists when a 
query has multiple matching terms. 
In operation, when the query server 38 determines that a query contains 
both a matching and a non-matching search term, the spell correction 
process 48 initially accesses the correlation table 50 to extract the 
associated related terms list 62. If the query includes multiple matching 
search terms, the process 48 obtains the related terms lists 62 for each 
matching term and merges these lists together (as described below) to 
generate a composite related terms list. As indicated above, the process 
then compares the spellings of the resulting related terms to the 
spelling(s) of the non-matching term(s), and either replaces or deletes 
each non-matching term from the query. During the spelling comparison 
process, each non-matching term is preferably compared only to those 
related terms that fall within the non-matching term's search field. 
An important benefit of this method is that it is particularly well suited 
for correcting misspellings of terms that do not appear in the dictionary. 
The technique is thus particularly useful for identifying items that tend 
to be characterized by non-dictionary terms. Such items include, for 
example, products sold by online merchants, court opinions (commonly 
identified by the names of the parties), and businesses and their Web 
sites. In the context of an online book/music store, for example, the 
method is useful for correcting misspellings of proper names of 
authors/artists and fanciful terms that appear within titles. For example, 
a user searching for a book by Jon Krakauer might be able to find the book 
by typing in a few words of the title plus a misspelled version of 
"Krakauer." The likelihood that the search will identify the desired book 
will be particularly high if a relatively large number of users have 
recently searched for the same book. 
Although the method preferably uses search term correlations that are based 
on prior query submissions, it should be understood that the correlation 
data can alternatively be generated from other sources. For example, the 
correlation data can be generated by processing the information in the 
bibliographic database 40 to identify terms that appear together within 
the same item record, title, item description, book review or other 
database field; in performing this process, each item can be accorded a 
weight that is proportional, for example, to the number of units of that 
item sold over the last week. 
In addition, the method can be modified to incorporate other types of 
correlations, including correlations based on other types of historical 
user actions. For example, in extracting correlation data from the query 
log 36, greater weight can be given to query submissions which, based on 
the users' subsequent actions, can be deemed to have produced a successful 
query result. Such success can be presumed, for example, if the user 
views, purchases or adds to a shopping cart an item located by the search. 
The disclosed search engine also preferably uses historical query 
submissions and item selections to rank query results for presentation to 
the user. A preferred method for ranking search results based on such data 
is disclosed in U.S. patent application Ser. No. 09/041,081 filed Mar. 10, 
1998. The search engine also preferably uses historical query data to 
identify related search terms for helping the user refine a search query, 
such as when the search query produces a long list of items. A preferred 
method for generating and displaying related search terms is disclosed in 
U.S. Provisional Appl. Ser. No. 60/089,244, filed Jun. 15, 1998. The 
disclosures of the aforesaid patent applications are hereby incorporated 
herein by reference in their entirety. 
As described in the above-referenced provisional application, the search 
engine generates and displays related search terms as follows. For a 
single-term query, the top N related search terms (those with the highest 
scores) for the query term are simply read from the table 50 and presented 
to the user, where N can be any desired number. For a multiple-term query, 
the search engine takes the intersection of the related terms lists 62 for 
the respective query terms, and presents any resulting terms to the user; 
for example, if a user conducts a search for "outdoor trail," and the 
terms "outdoor" and "trail" have the related terms lists 62 indicated 
below, the intersecting terms "bike" and "sports" would be presented to 
the user as suggested refinements. 
OUTDOOR: BIKE, DINING, EDUCATION, FITNESS, FURNITURE, SPORTS, TRAIL, 
VACATION 
TRAIL: BLAZING, BIKE, MAP, MIX, OUTDOOR, SPORTS, WAGON, YUKON 
Each related search term is preferably presented to the user, in 
conjunction with the search results, using a respective textual link that 
is selectable to initiate the refined search. In the above example, the 
user would thus be presented with one link for initiating the refined 
search "OUTDOOR TRAIL BIKE," and another link for initiating the refined 
search "OUTDOOR TRAIL SPORTS." 
With further reference to FIG. 1, the Web server 32, query server 38, table 
generation process 46, and database software run on one or more 
Unix.TM.-based servers and workstations (not shown) of the Web site 30. 
The correlation table 50 is stored in RAM (random access memory) on the 
same workstation as that used to implement the query server 38. 
III. Query Processing Method 
The spelling correction process will now be described in further detail 
with reference to FIG. 4, which is a flow diagram of the steps performed 
by the query server 38 (FIG. 1) when a user submits a query. To illustrate 
this process, it will be assumed that the user is searching for books 
about hiking the Appalachian Trail and has typed the following query into 
the subject field 44 (FIG. 2): "hike Appalatian trail." It will also be 
assumed that "Appalatian" is a non-matching term (although "Appalachian" 
is matching), and that "hike" and "trail" are matching terms having the 
following related terms lists: 
HIKE: CAMPING (235), WALKS (160), TRAIL (150) 
TRAIL: BIKE (200), APACHIAN (165), WALKS (50) 
It will also be assumed that all of the above keywords and related terms 
have a field prefix of "S" for "subject." 
As indicated by step 70, the query server 38 initially applies the query to 
bibliographic database 40. As depicted by steps 72 and 74, if one or more 
items are found, the query server returns a list of these items the Web 
server 32. The Web server 32 in-turn incorporates this list into one or 
more search results pages, or, if only one item is located, returns the 
product information page for that item. In addition, the Web server 
records the number of items found in the query log 36 (see FIG. 5). In the 
present example, no items would be found since "Appalatian" does not exist 
as a subject word in the bibliographic database 40. 
If, as in the present example, the number of items found is zero in step 
72, the query server 38 determines whether the query includes both 
matching and non-matching terms (step 76). If it does, the query server 38 
invokes its spelling correction process 48 (steps 80-90 in FIG. 4) to 
attempt to correct the non-matching term(s). If it does not, a message is 
returned to the user (step 78) indicating that no exact matches were 
found; in this event, the query server 38 may also generate and return a 
list of "close matches," which may include items that contain only a 
subset of the matching terms. The query sever 38 could additionally or 
alternatively be configured to invoke an alternative spelling correction 
method (not shown) to attempt to correct any non-matching term(s). In the 
present example, the spell correction process 48 would be invoked since 
the query includes both matching and non-matching terms. 
The spell correction process begins in step 80 by retrieving the related 
terms list for each matching term from the correlation table 50. If no 
related term lists (table entries) are found during step 80, the process 
preferably returns a null query result message and terminates (not shown). 
Alternatively, the process could use an alternative spelling correction 
method to attempt to correct the non-matching term(s), or could generate 
and return a list of "close matches." 
If, as in the present example, the query includes multiple matching terms, 
the related terms lists for these terms are merged together in step 80 to 
form a composite related terms list. The lists may be merged, for example, 
by combining the lists while summing the correlation scores of any 
intersecting terms (terms which appear in multiple lists), and then 
sorting the composite list in order of highest-to-lowest frequency score. 
In the present example, this method would produce the following related 
terms list: CAMPING (235), WALKS (210), BIKE (200), APACHIAN (165), 
TRAIL (150). The sorted composite list can be truncated to reduce the 
processing burden of the subsequent steps. 
The spell correction process then enters into a loop (steps 84-90) in which 
the spellings of the non-matching term(s) and the related terms are 
compared. In each pass of this loop, the process compares a non-matching 
term to the list of related terms one-by-one (step 84), excluding any 
related terms having field prefixes that do not correspond to the search 
field of the non-matching term. The comparisons are performed using an 
anagram-type function which compares two character strings and returns a 
numerical similarity score. The similarity score indicates the degree of 
similarity between the spellings of the two strings, and thus indicates 
the likelihood that the given related term is a suitable replacement for 
the current non-matching term. A similarity score of zero indicates the 
highest degree of similarity, and progressively higher scores indicate 
progressively lower degrees of similarity. 
The steps performed by the spelling comparison function to compare STRING1 
to STRING2 are set forth below, in which the variable RESULT represents 
the score that is returned by the function. A code listing of a C++ 
implementation of the method (implemented as two separate functions) is 
attached as an appendix. 
Step 1: Sort STRING1 and STRING2 in alphabetical order. 
Step 2: Initialize POINTER1 to first character of sorted STRING1; POINTER2 
to first character of sorted STRING2; and RESULT to zero. 
Step 3: Compare respective characters pointed to by POINTER1 and POINTER2 
without regard to case. If equal, advance both pointers to next character 
of string; else, advance pointer which points to alphabetically lower 
character and increment RESULT by 1. 
Step 4: Repeat step 3 until a pointer is advanced beyond the last character 
of its respective string. 
Step 5: If one of the pointers is still pointing to a character, add to 
RESULT the number of characters (including the pointed-to character) 
remaining in that string. 
In the present example, step 84 results in the non-matching term APATIAN 
being compared to the terms CAMPING, WALKS, BIKE, APACHIAN, and TRAIL. 
The scores generated by these comparisons are listed in the table below. 
TABLE 
______________________________________ 
SORTED STRING COMED 
SIMILARITY 
RELATED TERM 
TO "AAAAILNPPT" SCORE 
______________________________________ 
CAMPING ACGIMNP 9 
WALKS AKLSW 11 
BIKE BEIK 12 
APACHIAN 
AAAACHILNPP 3 
TRAIL AILRT 7 
______________________________________ 
In step 86, the scores are evaluated to determine whether any of the 
related terms has a sufficiently similar spelling to be a candidate 
replacement term. In the preferred embodiment, a related term is deemed to 
be similar (and thus a candidate replacement) if its score is less than or 
equal to half the length of the non-matching term (the "similarity 
threshold"). In the present example, the term "Appalachian" satisfies the 
similarity test (since 3.ltoreq.5) and the remaining related terms do not. 
To increase the efficiency of the spelling comparison function, processing 
in step 3 can be halted once RESULT exceeds the similarity threshold. 
Efficiency could also be increased by halting the processing of additional 
terms once a term with a sufficiently low score (e.g., 0) has been 
identified. 
If at least one related term satisfies the similarity test in step 86, the 
non-matching term is replaced with related term having the lowest score 
(step 88). If multiple related terms share the lowest score in step 88, 
the term which falls first in the related terms list (and thus has the 
highest frequency score) is used as the replacement. 
If none of the related terms satisfies the similarity test in step 86, the 
non-matching term is deleted from the query. Alternatively, a null query 
result message could be returned to the user at this point. Once all of 
the non-matching terms have been either replaced or deleted, the search is 
re-attempted using the modified query and the result is returned to the 
user (step 94). 
If the re-attempted search in step 94 produces one or more items, the items 
are presented together with a message indicating the modification(s) made 
to the query. In the present example, this message might read as follows: 
No books were found which include the subject word "Appalatian." However, 
we reattempted your search using the term "Appalachian" in place of 
"Appalatian" and found the following titles. 
The search results page also preferably displays and allows the user to 
edit the modified query, so that the user can effectively reject the 
search term replacement(s) and/or otherwise revise the query. 
If the re-attempted search in step 94 does not produce any exact matches, 
the user may be presented with a list of close matches, or may simply be 
notified that no matches were found. As an alternative, additional 
replacements and searches can be attempted. 
As will be appreciated from the foregoing, the FIG. 4 process can be 
altered in any of variety of ways to achieve a desired objective. For 
example, separate correlation tables could be generated and used for 
different types of items (e.g., books v. music) and/or different types of 
fields (e.g., title v. subject). Further, the spell correction process 
could be used only to correct misspellings within a specific field (such 
as the author field 42), or could be applied only to non-matching terms 
that do not appear within a dictionary of terms. 
IV. Generation of Correlation Table 
The table generation process 46 (FIG. 1) will now be described with 
reference to FIGS. 5 and 6. 
The table generation process 46 is implemented as an off-line process which 
is executed periodically, such as once per day, to generate a new 
correlation table 50. As described above, the process generates the table 
from the M most recent daily query log files 36(1)-36(M). Using a 
relatively small M (e.g. 5) tends to produce correlation data that heavily 
reflects short term buying trends (e.g., new releases, weekly 
best-sellers, etc.), while using a larger M (e.g., 100) tends to produce a 
more comprehensive database. A hybrid approach can alternatively be used 
in which the table is generated from large number of log files, but in 
which the most recent log files are given greater weight. For example, 
queries submitted during the last week can be counted three times when 
generating the correlation scores 64, while queries submitted from one 
week to one month ago can be counted only once. 
FIG. 5 illustrates the general format of the query log files. Each entry in 
the log (four entries shown) includes information about a particular HTTP 
(Hypertext Transfer Protocol) transaction. For example, entry 100 
indicates that at 2:23 AM on Feb. 13, 1998, user 29384719287 submitted the 
query {author=Seagal, title=Human Dynamics} from the book search page and 
that two items were found that matched the query. The ITEMS.sub.-- FOUND 
values in the log preferably indicate the number items that exactly 
matched the original query, and thus do not reflect either "close matches" 
or matches resulting from spelling corrections. 
Entry 102 indicates that the same user selected an item having an ISBN of 
1883823064 about twenty seconds later, and that this selection was made 
from a search results page (as is evident from the "HTTP.sub.-- REFERRER 
line). Other types of user actions, such as a request to place an item in 
a shopping cart or to purchase an item, are similarly reflected within the 
log files. As indicated by the above example, a given user's navigation 
path can be determined by comparing entries within the log 36. 
FIG. 6 illustrates the sequence of steps performed by the table generation 
process 46. In this example, it is assumed that the process is executed 
once per day at midnight, just after the most recent daily log file is 
closed. It is also assumed that the M-1 most recent daily log files have 
already been processed using steps 110-114 of the process to generate 
respective daily results files. 
In step 110, the process parses the new daily log file to extract all query 
submissions for which ITEMS.sub.-- FOUND&gt;0. Ignoring the query submissions 
that produced null query result (ITEMS.sub.-- FOUND=0) provides the 
important benefits of (1) preventing non-matching terms from being added 
to the correlation table--either as keywords or as related terms--and (2) 
excluding potentially "weak" correlations between matching terms from 
consideration. 
In step 112, the entries extracted in step 110 are processed to correlate 
search terms by frequency of occurrence within the same query. This step 
involves counting, for each pair of search terms that appeared within the 
same query at least once, the number of times the two terms appeared 
together throughout the day. During this process, identical terms that 
were submitted in different search fields are treated as different terms. 
For example, the term TRAIL with a field prefix of "T" would be treated as 
different from TRAIL with a prefix of "S." 
As indicated above, any of a variety of other factors can be taken into 
consideration during the step 112 process. For example, a given query 
submission can be counted twice if the user subsequently selected an item 
from the search results page, and counted a third time if the user then 
purchased the item or added the item to a personal shopping cart. 
Extrinsic correlation data can also be incorporated into the process. The 
results of step 112, which are in the general form of the FIG. 3 
correlation table, are saved as a daily results file. 
In step 116, the daily results file created in step 114 is merged with the 
last M-1 daily results files to produce the correlation table 50. As part 
of this process, the related terms lists are truncated to a fixed length 
of N, and the resulting correlation data is stored in a B-tree data 
structure for efficient look-up. The new correlation table 50 is then 
written to RAM in place of the existing correlation table. 
Although this invention has been described in terms of certain preferred 
embodiments, other embodiments that are apparent to those of ordinary 
skill in the art are also within the scope of this invention. Accordingly, 
the scope of the present invention is intended to be defined only by 
reference to the appended claims. 
In the claims which follow, reference characters used to denote process 
steps are provided for convenience of description only, and not to imply a 
particular order for performing the steps. 
______________________________________ 
APPENDIX 
______________________________________ 
char *sort.sub.-- string(char *string.sub.-- to.sub.-- sort) { 
qsort(string.sub.-- to.sub.-- sort, str1en(string.sub.-- to.sub.-- 
sort), 
sizeof(char), qsort.sub.-- char.sub.-- compare); 
return string.sub.-- to.sub.-- sort; 
int score.sub.-- sorted.sub.-- strings(char* string1, char* string2) { 
int result = 0; 
int finished = FALSE; 
int compare = 0; 
unsigned char* str1.sub.-- ptr = (unsigned char*) string1; 
unsigned char* str2.sub.-- ptr = (unsigned char*) string2; 
while (!finished) { 
if (*str1.sub.-- ptr == 0 .vertline..vertline. *str2.sub.-- ptr == 0) 
finished = TRUE; 
/* The scoring function compares two characters; if they are the 
same, both pointers are incremented to move on, otherwise 
only the lesser pointer is incremented and 
compared again */ 
if (!finished) { 
compare = qsort.sub.-- char.sub.-- compare((void*) 
str1.sub.-- ptr, (void*)str2.sub.-- ptr); 
if ( compare &lt; 0) { 
str1.sub.-- ptr++; 
result++; 
} else if ( compare &gt; 0) { 
str2.sub.-- ptr++; 
result++; 
} else if ( compare == 0) { 
str1.sub.-- ptr++; 
str2.sub.-- ptr++; 
} 
} 
} 
/* Add to the score any remaining results */ 
while (*str1.sub.-- ptr++ != NULL) { 
result++; 
} 
while (*str2.sub.-- ptr++ != NULL) { 
result++; 
} 
return result; 
} 
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