System, method and computer program for automated collaborative filtering of user data

• US 20020065797A1
• Filed: 11/30/2000
• Published: 05/30/2002
• Est. Priority Date: 11/30/2000
• Status: Abandoned Application

• Current Assignee: WizSoft Ltd.
• Original Assignee: WizSoft Ltd.
• Inventors: Ravid, Hadas, Meidan, Abraham, Oren, Zbeida
• Application Number:
  US09/726,046
• Publication Number:
  US 20020065797A1
• Time in Patent Office: Days
• US Class Current: 707/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A system and method for collaborative filtering of data, such that accurate real time recommendations can be provided to users, without prior rating by users. The invention'"'"'s main purpose is to discover the purchasing patterns of the users (customers) of a particular vendor (this includes stores or other points of sale, such as service businesses), whether they are virtual places on the Internet or conventional offline places.

The present invention operates in two stages: In the preliminary stage the system reads the previous sales transactions, and makes various queries on previously collected user data, deriving rules. In the second phase, a user making a new request sends the request to the software component, where it is processed in relation to the rules. The result of this processing is a derivation of predictions and alerts concerning the users'"'"' information or purchase requests. The system also checks user baskets in order to predict if there are preferred items that have not been included, or determine whether there are unexpected items that may indicate buying mistakes or security warnings.

The best modes of the invention are for use in online virtual stores and offline conventional stores. Such stores may be shops, vendors or service providers.

Claims

1. A method for processing a recommendation for a user, according to collaborative filtering rules, comprising the steps of:

i) For each item (A), querying relative frequency of said item out of all baskets (this frequency will designated later by;

F(A));

ii) For each said item, A, and customer, C, querying relative frequency of said item out of baskets of said customer, (this frequency will be designated later by;

Fc(A));

iii) For each pair of items, A and B, querying relative frequency of baskets containing said item A out of baskets containing both item A and B, (this frequency will be designated later by;

F(A&

B)); and

establishing a rule that if a basket contains item B but not item A, recommend item A, in the case where a difference between F(A&

B) and F(A) is above a predetermined threshold; and

iv) For each pair of items, A and B, and a customer, C, querying the relative frequency of said baskets containing item A out of said baskets of customer C containing item A and item B, (this frequency will designated later by;

Fc(A&

B)); and

establishing a rule that if a basket belongs to customer C and contains item B but not item A, recommend item A, in the case where a difference between Fc(A&

B) and Fc(A) is above a predetermined threshold.

2. The method of claim 1, wherein step (iii) further includes establishing a rule that if a basket contains item A and B, alert that item A should not be in said basket, in the case where a difference between F(A) and F(A&

B) is above a predetermined threshold.

4. The method of claim 1, wherein the processing of the recommendation for said user further includes the steps of:

v) If said customer is anonymous, applying said rules of claim 1c, such that a recommendation is created for said customer,

vi) If said customer is an old customer applying said rules in claim 1d, such that a recommendation is created for said customer,

5. The method of claim 1, wherein said basket includes data selected from the group consisting of purchase requests, information requests, security requests and sales requests.

3. The method of step 1, wherein step (iv) further includes establishing a rule that if a basket belongs to customer C and contains item A and B, alert that item A should not be in said basket”

, if the difference between Fc(A) and Fc(A&

B) is above a predetermined threshold.