Source: http://www.google.com/patents/US7580929?dq=6,219,045
Timestamp: 2014-07-29 16:58:49
Document Index: 267868796

Matched Legal Cases: ['Application No. 200510085373', 'Application No. 200510085370', 'Application No. 200510085371', 'Application No. 200510085372', 'Application No. 2005203237', 'Application No. 2005203238', 'Application No. 2005203239', 'Application No. 2005203240']

Patent US7580929 - Phrase-based personalization of searches in an information retrieval system - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsAn information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. Related phrases and phrase extensions are also identified. Phrases...http://www.google.com/patents/US7580929?utm_source=gb-gplus-sharePatent US7580929 - Phrase-based personalization of searches in an information retrieval systemAdvanced Patent SearchPublication numberUS7580929 B2Publication typeGrantApplication numberUS 10/900,039Publication dateAug 25, 2009Filing dateJul 26, 2004Priority dateJul 26, 2004Fee statusPaidAlso published asUS20080319971Publication number10900039, 900039, US 7580929 B2, US 7580929B2, US-B2-7580929, US7580929 B2, US7580929B2InventorsAnna Lynn PattersonOriginal AssigneeGoogle Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (117), Non-Patent Citations (50), Referenced by (20), Classifications (10), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetPhrase-based personalization of searches in an information retrieval systemUS 7580929 B2Abstract An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. Related phrases and phrase extensions are also identified. Phrases in a query are identified and used to retrieve and rank documents. Phrases are also used to cluster documents in the search results, create document descriptions, and eliminate duplicate documents from the search results, and from the index.
monitoring a plurality of documents accessed by a user;
identifying a plurality of first phrases present in one or more of the accessed documents;
for each of the identified first phrases, identifying one or more corresponding first related phrases, wherein the one or more first related phrases are related to the corresponding identified first phrase;
storing a user model associated with the user, and comprising a plurality of the first related phrases;
receiving a query from the user, the query including one or more second phrases;
selecting search results comprising a plurality of documents responsive to the query;
identifying, by operation of a processor configured to manipulate data within a computer system, one or more second related phrases that are related to the second phrase(s) of the query and that are present in the user model;
weighting a plurality of scores of a corresponding plurality of the search results according to the identified one or more second related phrases;
ranking the plurality of the search results for presentation to the user according to their weighted scores, to provide personalized search results; and
presenting the personalized search results to the user.
2. The method of claim 1, wherein a document accessed by the user comprises a document printed by the user.
3. The method of claim 1, wherein a document accessed by the user comprises a document saved by the user.
4. The method of claim 1, wherein a document accessed by the user comprises a document stored as a favorite or link.
5. The method of claim 1, wherein a document accessed by the user comprises a document sent by email by the user.
6. The method of claim 1, wherein a document accessed by the user comprises a document maintained in a browser window for a predetermined amount of time.
7. The method of claim 1, wherein the documents accessed by the user are substantially only those documents accessed during a current session.
8. The method of claim 1, wherein selecting search results comprising a plurality of documents responsive to the query comprises:
identifying an incomplete phrase in the query;
replacing the incomplete phrase with a phrase extension, wherein the phrase extension of the incomplete phrase is a super-sequence of the incomplete phrase that begins with the incomplete phrase, and wherein the incomplete phrase predicts the phrase extension; and
selecting documents from the document collection containing the phrase extension.
9. The method of claim 8, wherein the incomplete phrase predicts the phrase extension based on a measure of an actual frequency of the phrase extension being greater than an expected frequency of the phrase extension in the document collection.
a related phrase gk is related to a phrase gj in a document when an information gain I of gk with respect to gj exceeds a predetermined threshold, the information gain I being a function of A(j,k) and E(j,k), where A(j,k) is a measure of an actual co-occurrence rate of gj and gk in a plurality of documents in the documents collection and E(j,k) is an expected co-occurrence rate gj and gk in a plurality of documents in the documents collection.
11. A method of personalizing a search of a document collection to a user, the method comprising:
identifying, by operation of a processor configured to manipulate data within a computer system, one or more second related phrases that are related to the second phrase(s) of the query and that are present in the user model, comprising:
for each phrase of the query, accessing a related phrase bit vector for the phrase of the query, wherein each bit of the related phrase bit vector indicates the presence or absence of a second related phrase of the phrase of the query;
determining from the related phrase bit vector which of the second related phrases are present in the user model; and
forming a related phrase bit mask corresponding to the second related phrases that are present in the user model;
12. The method of claim 11, wherein weighting a plurality of scores of a corresponding plurality of the search results according to the identified one or more second related phrases, comprises:
accessing a related phrase bit vector for a document and query phrase; and
weighting the related phrase bit vector for the document using the related phrase bit mask.
13. A method of personalizing a search of a document collection to a user, the method comprising:
storing a user model associated with the user, and comprising a plurality of cluster counts, each cluster count associated with a predetermined cluster that includes a plurality of first related phrases, and storing a count of a number of instances of first related phrases of the cluster appearing in a document accessed by the user;
weighting a plurality of scores of a corresponding plurality of the search results according to the cluster counts of the identified one or more second related phrases;
14. The method of claim 13, wherein a document accessed by the user comprises a document printed by the user.
15. The method of claim 13, wherein the document accessed by the user comprises a document saved by the user.
16. The method of claim 13, wherein the document accessed by the user comprises a document stored as a favorite or link.
17. The method of claim 13, wherein the document accessed by the user comprises a document sent by email by the user.
18. The method of claim 13, wherein the document accessed by the user comprises a document maintained in a browser window for a predetermined amount of time.
19. The method of claim 13, wherein selecting search results comprising a plurality of documents responsive to the query comprises:
20. The method of claim 19, wherein the incomplete phrase predicts the phrase extension based on a measure of an actual frequency of the phrase extension being greater than an expected frequency of the phrase extension in the document collection.
21. A computer readable storage medium storing a computer program executable by a processor for personalizing a search of a document collection to a user, the operations of the computer program comprising:
22. The computer readable storage medium of claim 21, wherein identifying one or more second related phrases that are related to the second phrase(s) of the query and that are present in the user model, comprises:
for each phrase of the query, accessing a related phrase bit vector for the phrase of the query, wherein each bit of the related phrase bit vector indicates the presence or absence of a related phrase of the phrase of the query;
forming a related phrase bit mask corresponding to the related phrases that are present in the user model.
23. A computer readable storage medium storing a computer program executable by a processor for personalizing a search of a document collection to a user, the operations of the computer program comprising:
storing a user model associated with the user, and comprising a plurality of cluster counts, each cluster count associated with a predetermined cluster that includes a plurality of the first related phrases, and storing a count of a number of instances of the first related phrases of the cluster appearing in a document accessed by the user;
24. The computer readable storage medium of claim 23, wherein selecting search results comprising a plurality of documents responsive to the query comprises:
replacing the incomplete phrase with a phrase extension, wherein the phrase extension of the incomplete phrase is a super-sequence of the incomplete phrase that begins with the incomplete phrase, and wherein the incomplete phrase predicts the phrase extension based on a measure of an actual frequency of the phrase extension being greater than an expected frequency of the phrase extension in the document collection; and
25. A computer implemented system for personalizing a search of a document collection to a user, comprising:
a user model associated with the user, stored in a storage medium and comprising a plurality of first related phrases contained in documents accessed by the user, wherein the first related phrases are identified as related to one or more first phrases in documents that have been accessed by a user; and
a query processing system executed by a computer and configured to:
receive a query from the user, wherein the query includes one or more second phrases;
identify one or more second related phrases that are related to the second phrase(s) of the query and that are present in the user model,
weight a plurality of scores of a corresponding plurality of the search results according to the identified one or more second related phrases,
26. The system of claim 25, wherein the query processing system identifies phrases that are related to the query and present in the user model by:
for each phrase of the query, accessing a related phrase bit vector for the query phrase, wherein each bit of the related phrase bit vector indicates the presence or absence of a related phrase of the phrase of the query;
27. A computer implemented system for personalizing a search of a document collection to a user, comprising:
a user model associated with the user, stored in storage medium and comprising a plurality of cluster counts, each cluster count associated with a predetermined cluster that includes a plurality of related first phrases, and storing a count of a number of instances of related first phrases of the cluster appearing in a document accessed by the user; and
identify one or more second related phrases that are related to the second phrase(s) of the query and associated with cluster counts in the user model,
weight a plurality of scores of a corresponding plurality of the search results according to the cluster counts of the identified one or more second related phrases,
28. The system of claim 27, wherein the query processing system selects search results comprising a plurality of documents responsive to the query by:
Phrase Identification in an Information Retrieval System, application Ser. No. 10/900,021 filed on Jul. 26, 2004; Phrase-Based Indexing in an Information Retrieval System, application Ser. No. 10/900,055 filed on Jul. 26, 2004; Phrase-Based Searching in an Information Retrieval System, application Ser. No. 10/900,041 filed on Jul. 26, 2004; Automatic Taxonomy Generation in Search Results Using Phrases, application Ser. No. 10/900,259 filed on Jul. 26, 2004; Phrase-Based Generation of Document Descriptions, application Ser. No. 10/900,075 filed on Jul. 26, 2004; and Phrase-Based Detection of Duplicate Documents in an Information Retrieval System, application Ser. No. 10/900,012 filed on Jul. 26, 2004, all of which are co-owned, and incorporated by reference herein. FIELD OF THE INVENTION The present invention relates to an information retrieval system for indexing, searching, and classifying documents in a large scale corpus, such as the Internet.
a) number of documents containing phrase, P(p)<2; and b) number of interesting instances of phrase, M(p)=0. These conditions indicate that the phrase is both infrequent, and not used as indicative of significant content and again these thresholds may be scaled per number of documents in the partition.
The final step of this stage is to prune the good phrase list 208 to remove incomplete phrases. An incomplete phrase is a phrase that only predicts its phrase extensions, and which starts at the left most side of the phrase (i.e., the beginning of the phrase). The �phrase extension� of phrase p is a super-sequence that begins with phrase p. For example, the phrase �President of� predicts �President of the United States�, �President of Mexico�, �President of AT&T�, etc. AU of these latter phrases are phrase extensions of the phrase �President of� since they begin with �President of� and are super-sequences thereof.
This high threshold is used to identify the co-occurrences of good phrases that are well beyond the statistically expected rates. Statistically, it means that phrases gj, and gk co-occur 100 times more than the expected co-occurrence rate. For example, given the phrase �Monica Lewinsky� in a document, the phrase �Bill Clinton� is a 100 times more likely to appear in the same document, then the phrase �Bill Clinton� is likely to appear on any randomly selected document. Another way of saying this is that the accuracy of the predication is 99.999% because the occurrence rate is 100:1.
For each related phrase m in Rj, the indexing system 110 determines if each of the other related phrases in R is also related to gj. Thus, if I(gk, gj) is also non-zero, then gj, gk, and gj are part of a cluster. This cluster test is repeated for each pair (gj, gm) in R.
entry G[row j,col. k]=(I(j,k), clusterNumber, clusterBitVector)
For a given-good phrase gj then the ordering of the related phrases by information gain provides a taxonomy for naming the clusters of the phrase: the cluster name is the name of the related phrase in the cluster having the highest information gain.
In a given phrase window 302, identify all good phrases in the window, starting at position i. Each good phrase is denoted as gj. Thus, g1 is the first good phrase, g2 would be the second good phrase, and so forth.
For each good phrase gj (example g1 �President� and g4 �President of ATT�) post the document identifier (e.g., the URL) to the posting list for the good phrase gi in the index 150. This update identifies that the good phrase gi appears in this specific document.
Phrase gj: list: (document d, [list: related phase counts][related phrase information])
In one embodiment, the related phrase information is a related phase bit vector. This bit vector may be characterized as a �bi-bit� vector, in that for each related phrase gk there are two bit positions, gk−1, gk−2. The first bit position stores a flag indicating whether the related phrase gk is present in the document d (i.e., the count for gk in document d is greater than 0). The second bit position stores a flag that indicates whether a related phrase gl of gk is also present in document d. The related phrases gj of a related phrase gk of a phrase gj are herein called the �secondary related phrases of gj� The counts and bit positions correspond to the canonical order of the phrases in R (sorted in order of decreasing information gain). This sort order has the effect of making the related phrase gk that is most highly predicted by gj associated with the most significant bit of the related phrase bit vector, and the related phrase gj that is least predicted by gj associated with the least significant bit.
gj: document d: related phrase counts:={3,4,3,0,0,2,1,1,0}related phrase bit vector:={11 11 10 00 00 10 10 10 01}
As described above, for any given phrase gj, each document d in the gj's posting list has an associated related phrase bit vector that identifies which related phrases gk and which secondary related phrases gj are present in document d. The more related phrases and secondary related phrases present in a given document, the more bits that will be set in the document's related phrase bit vector for the given phrase. The more bits that are set, the greater the numerical value of the related phrase bit vector.
To cull these latter types of documents, the search system 120 uses the cluster information in the cluster bit vectors of the query phrases, and removes any document in which there are more than a threshold number of clusters in the document. For examples the search system 120 can remove any documents that contain more than two clusters. This cluster threshold can be predetermined, or set by the user as a search parameter.
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