EP1024437A2
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EP
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Multi-modal information access
Assignee: Xerox Corp
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Filing: 2000-01-24
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Granted: —
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Expires: 2020-01-24
Abstract
Abstract A system and method for browsing, retrieving, and recommending information from a collection uses multi-modal features of the documents in the collection, as well as an analysis of users' prior browsing and retrieval behavior. The system and method are premised on various disclosed methods for quantitatively representing documents in a document collection as vectors in multi-dimensional vector spaces, quantitatively determining similarity between documents, and clustering documents according to those similarities. The system and method also rely on methods for quantitatively representing users in a user population, quantitatively determining similarity between users, clustering users according to those similarities, and visually representing clusters of users by analogy to clusters of documents.
Inventors
- 👤 Francine R. Chen
- 👤 Hinrich Schuetze
- 👤 Ullas Gargi
- 👤 James E. Pitkow
- 👤 Peter L. Pirolli
- 👤 Ed H. Chi
- 👤 Jun Li
- 👤 Leslie T. Niles
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-10-29 16:45
CPC Classifications
G; G06; G06F; G06F16/00; G06F16/50; G06F16/58; G06F16/583; G06F16/5838; G06F16/30; G06F16/35; G06F16/355; G06F16/90; G06F16/904
Claims Analysis
Total Claims: 10
Claim 1
Independent Claim
54 words
A method for information browsing using multi-modal features,
comprising the steps of: isolating a plurality of features corresponding to each of a first plurality of
objects in a collection; searching the collection to obtain search results; clustering a second plurality of objects into a plurality of clusters; and presenting the clusters to a user.
Claim 2
Independent Claim
64 words
A system adapted for information browsing using multi-modal
features, comprising: storage for a document collection, wherein the document collection includes a
plurality of documents each having a plurality of features; a database adapted to store a quantitative representation of each feature
corresponding to each document in the document collection; and a processor adapted to facilitate clustering the documents according to at least
one feature.
Claim 3
Independent Claim
63 words
A method for providing document recommendations from a document
collection based on multi-modal user clusters, comprising the steps of: identifying an initial set of users representing a subset of all possible users; clustering the initial set of users into a plurality of user clusters; identifying a new user; collecting information from the new user; and assigning the new user to a user cluster.
Claim 4
Independent Claim
55 words
A method for quantitatively representing objects in a vector space,
comprising the steps of: identifying an object to be processed from a plurality of objects; extracting a feature corresponding to the object from the plurality of objects; converting the feature to at least one vector; and associating the at least one vector with the object.
Claim 5
Independent Claim
76 words
A method for calculating the similarity between two objects in a
collection of objects, wherein each object is associated with at least one multi-dimensional
vector representative of a feature of the object, comprising the steps of: identifying a first vector corresponding to a first feature of a first object and a
second vector corresponding to a first feature of a second object; and computing a first distance metric between the first vector and the second
vector.
Claim 6
Independent Claim
88 words
A method for calculating the similarity between two objects in a
collection of objects, wherein each object is associated with a plurality of multi-dimensional
vectors representative of a plurality of corresponding features of the
object, comprising the steps of: for each feature, identifying a first vector corresponding to a first object and a
second vector corresponding to a second object, for each feature, computing a distance metric between the first vector and the
second vector; and summing the distance metrics for each feature into an aggregate distance
metric.
Claim 7
Independent Claim
63 words
A method for calculating the similarity between two users in a user
population, wherein each user is associated with a multi-dimensional vector
representative of a user feature, comprising the steps of: identifying a first vector corresponding to a first user and a second vector
corresponding to a second user; and computing a first distance metric between the first vector and the second
vector.
Claim 8
Independent Claim
69 words
A method for selecting a set of initial cluster centers in clustering a
collection of objects in a multi-dimensional vector space, comprising the steps of: selecting a first number of first objects from the collection; computing a vector centroid of the first objects; selecting a second number of second objects from the collection; and identifying a second number of initial cluster centers between the centroid and
the second objects.
Claim 9
Independent Claim
75 words
A method for clustering a collection of objects in a multi-dimensional
vector space, comprising the steps of: selecting a first number of first objects from the collection; computing a vector centroid of the first objects; selecting a second number of second objects from the collection; identifying a second number of initial cluster centers between the centroid and
the second objects; and performing iterated k -means clustering around the initial cluster centers to
cluster the objects.
Claim 10
Independent Claim
117 words
A method for visualizing clusters of users represented by way of
documents selected from a collection of documents, comprising the steps of: identifying a selected plurality of users in a user population, wherein the
plurality of users share an interest; for each user in the plurality of users, for each document in the collection,
identifying a corresponding access probability corresponding to the likelihood that the
user will access the document; for each document in the collection, calculating an aggregate access
probability across the users in the selected plurality of users, corresponding to the
likelihood that a user in the selected plurality will access the document; and visually displaying the aggregate probability for each document in the
collection.
References Cited (Backward Citations)
EP0542429A2
Cited
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