Method and system for distributing users to clusters

Abstract

Methods and apparatus, including systems and computer program products, to provide clustering of users in which users are each represented as a set of elements representing items, e.g., items selected by users using a system. In one aspect, a program operates to obtain a respective interest set for each of multiple users, each interest set representing items in which the respective user expressed interest; for each of the users, to determine k hash values of the respective interest set, wherein the i-th hash value is a minimum value under a corresponding i-th hash function; and to assign each of the multiple users to each of the respective k clusters established for the respective user, the i-th cluster being represented by the i-th hash value. The assignment of each of the users to k clusters is done without regard to the assignment of any of the other users to k clusters.

Description

[0001] This application is a PCT international application with an international filing date of August 15, 2006 and an international application number of PCT / US2006 / 031868, which entered the Chinese national phase with a national application number of 200680038100.7, entitled "Possibility of Set-Based Similarity A divisional application of the patent application for "Extending User Clustering". technical field [0002] The present invention relates to digital data processing, and more particularly to grouping users of computer applications or systems into clusters. Background technique [0003] The operation of grouping users into clusters serves several purposes. To achieve user personalization, for example, a well-known technique, collaborative filtering, involves clustering users and recommending to users items that other users in the user cluster have expressed interest in. A user may generally be considered to express interest in an item in a variety of ways, for exam...

AI Technical Summary

Problems solved by technology

However, these techniques have disadvantages

For example, the running time of HAC is O(n 2 ), which is difficult to achieve for hundreds of millions of values ​​of n; and the k-means algorithm needs to represent the mean of the data points, which is not feasible when the data points are sets

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