Clustering based personalized web experience
First Claim
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1. A personalization method, comprising:
- forming a personal profile for a user from the output of a first clustering algorithm applied to (1) a plurality of documents viewed by the user, and (2) one or more data streams comprising at least one of;
data entered by the user;
click stream data characterizing a series of web navigation actions by the user; and
purchase data identifying one or more items that have been purchased by the user; and
presenting content to the user as a function of selected data in the personal profile.
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Abstract
One embodiment of the present invention is a method for the customized presentation of one or more document streams. The method involves accepting or determining criteria characterizing information of interest to a user, and processing a stream of documents, wherein each document is tagged with one or more key content terms, and theme data is generated. The stream is filtered based on whether the criteria apply to each document, the documents in the filtered stream are clustered, and the clustered documents (including the theme data) are presented to the user via a visual user interface.
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Citations
54 Claims
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1. A personalization method, comprising:
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forming a personal profile for a user from the output of a first clustering algorithm applied to (1) a plurality of documents viewed by the user, and (2) one or more data streams comprising at least one of;
data entered by the user;
click stream data characterizing a series of web navigation actions by the user; and
purchase data identifying one or more items that have been purchased by the user; and
presenting content to the user as a function of selected data in the personal profile. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for the customized presentation of one or more document streams, comprising:
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accepting one or more user-provided criteria;
processing a stream of documents, the processing for each document in the stream including;
tagging the document with one or more key content terms; and
generating theme data for the document;
filtering the stream based on whether the criteria apply to the key content terms for each document;
clustering the filtered stream; and
presenting the clustered stream, including theme data for at least one presented document, to a user via a graphical user interface. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. A method, comprising:
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accessing a plurality of electronic documents;
attaching one or more key terms to each of the electronic documents to represent its content;
creating a personal profile for a user;
filtering the electronic documents as a function of the personal profile and the key terms;
applying a first soft clustering algorithm to the filtered electronic documents to cluster the filtered electronic documents into two or more content-based categories; and
presenting the two or more content-based categories to the user. - View Dependent Claims (28, 29, 30, 31)
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32. A clustering method, comprising:
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applying a first clustering algorithm to electronic data accessed by a user to form a user profile;
filtering electronic documents as a function of the user profile to retain a set of user-appropriate appropriate electronic documents; and
applying a second clustering algorithm to the set of user-appropriate electronic documents to produce one or more clusters. - View Dependent Claims (33, 34, 35)
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36. A system, comprising:
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a client computer, wherein the client computer accesses electronic documents and clusters data from the electronic documents to develop user criteria; and
a remote computer, wherein the remote computer accepts the user criteria, processes a stream of documents, filters the stream of documents based on whether the user criteria apply to each document in the stream;
clusters the filtered stream, and presents the clustered stream to the client computer.
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37. A system, comprising a processor and a computer-readable medium encoded with programming instructions executable by the processor to:
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access electronic documents;
tag each electronic document with one or more key content terms;
generate theme data for each electronic document;
filter the electronic documents based on whether preference criteria of a user apply to the key content terms of each electronic document;
apply a first clustering algorithm to the electronic documents to produce clusters; and
present the clusters, including theme data, to the user. - View Dependent Claims (38, 39)
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40. A method, comprising:
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a user at a computer accessing a plurality of electronic documents;
the user at the computer generating one or more data streams comprising at least one of;
data entered by the user;
click stream data characterizing a series of web navigation actions by the user; and
purchase data identifying one or more items that have been purchased by the user; and
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the computer capturing data from the plurality of electronic documents and the one or more data streams with a software agent on the computer; and
the computer displaying clusters of electronic articles, wherein the clusters are generated by applying a first clustering algorithm to filtered electronic articles, wherein the filtered electronic articles are generated by attaching tag data to electronic articles and filtering the electronic articles as a function of the tag data and a set of user criteria. - View Dependent Claims (41, 42, 43, 44, 45)
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46. An apparatus, comprising one or more processors and a memory encoded with programming instructions executable by the one or more processors to:
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accept one or more user-provided criteria;
process a stream of documents, wherein to process each document in the stream includes;
tagging the document with one or more key content terms; and
generating theme data for the document;
filter the stream based on whether the criteria apply to each document;
cluster the filtered stream; and
present the clustered stream, including the theme data, to the user via a graphical user interface. - View Dependent Claims (47, 48, 49)
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50. A method of clustering a collection of documents, comprising:
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creating an ordered list of w unique words in the collection of electronic documents;
initializing a set P of zero or more prototype vectors, each of a dimension w; and
for each document d in the collection of electronic documents;
a) generating a w-dimensional vector Id of numbers that each characterize the frequency in d of the word in the corresponding position in the ordered list;
b) for each prototype Pi;
i) determining a degree of membership of document d in Pi; and
ii) if the degree of membership is greater than a predetermined threshold ρ
, updating prototype Pi as a function of document d. - View Dependent Claims (51, 52, 53, 54)
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Specification