SYSTEM AND METHOD FOR COMPUTER-BASED MARKETING

• US 20010013009A1
• Filed: 05/19/1998
• Published: 08/09/2001
• Est. Priority Date: 05/20/1997
• Status: Abandoned Application

• Current Assignee: Adobe Systems Incorporated (Adobe Inc.)
• Original Assignee: Adobe Systems Incorporated (Adobe Inc.)
• Inventors: GREENING, DANIEL R., HEY, JOHN B.
• Application Number:
  US09/081,264
• Publication Number:
  US 20010013009A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A marketing system and method predicts the interest of a user in specific items—such as movies, books, commercial products, web pages, television programs, articles, push media, etc.—based on that user'"'"'s behavioral or preferential similarities to other users, to objective archetypes formed by assembling items satisfying a search criterion, a market segment profile, a demographic profile or a psychographic profile, to composite archetypes formed by partitioning users into like-minded groups or clusters then merging the attributes of users in a group, or to a combination. The system uses subjective information from users and composite archetypes, and objective information from objective archetypes to form predictions, making the system highly efficient and allowing the system to accommodate “cold start” situations where the preferences of other people are not yet known.

Claims

1. A method for predicting the reaction of a selected user in a group of users to an item not rated by the selected user in a set of items including items previously rated by the selected user, the method comprising the steps of:

defining, for each user in the group, and for each item in the set of items sampled by that user, a rating representing the reaction of the user to the item;

defining a plurality of objective archetypes, each representing a hypothetical user and associated with at least one item in the set;

defining, for each of the plurality of objective archetypes, a rating representing the hypothesized reaction of the represented hypothetical user to the associated at least one item;

selecting a set of mentors from the users in the group and from the plurality of objective archetypes based on the similarity of the ratings of each user in the group and each objective archetype to the ratings of the selected user;

successively pairing the selected user with each mentor and computing a similarity function representing the overall rating agreement for the pair;

predicting the rating of the selected user for the not rated item from the similarity functions and the mentors'"'"' ratings of that item

2. The method of

claim 1

, further comprising the step of:

defining an objective archetype representing a class of hypothetical users.

3. The method of

claim 1

, wherein the predicting step comprises the step of:

applying a prediction function to the similarity functions, the mentor'"'"'s ratings of the item not rated by the selected user, and a previously established prediction for the selected user'"'"'s rating of that item.

4. The method of

claim 1

, further comprising the step of:

combining a plurality of users in the group into a composite archetype having ratings reflecting the ratings of the combined users;

wherein the selecting step selects the set of mentors from the plurality of objective archetypes, the group of users, and the composite archetype.

5. The method of

claim 4

, further comprising the step of:

removing each of the users combined into the composite archetype from the group of users from which mentors are selected.

6. The method of

claim 4

, wherein the combining step combines one or more objective archetypes into the composite archetype.

7. The method of

claim 4

, wherein the combining step further comprises the steps of:

recording the ratings reflecting the combined users as a mean and a variance of the individual ratings; and

storing a confidence value with the mean and variance indicating a confidence that the ratings are accurate.

8. The method of

claim 1

wherein the similarity function computes an inverse of a weighted sum of normalized difference functions of ratings of items rated by the selected user mentor pair.

9. The method of

claim 1

, further comprising the step of:

storing the predicted rating of the selected user for use as a mentor in subsequent predictions.

10. The method of

claim 1

, wherein each rating is specified as a multidimensional value, with each dimension representing a different reaction type that led to the rating.

11. The method of

claim 1

, wherein computer program steps for performing the method are encoded on a computer-readable medium.

12. A system for predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, comprising:

a module for defining, for each item sampled by the selected user, a rating representing the reaction of the selected user to that item;

a module for defining a set of raters from the group of users, each rater in the set having a rating for one or more items sampled by the selected user, wherein at least one rater is an objective archetype having hypothetical user ratings for one or more items sampled by the selected user;

a module for successively pairing the selected user with each rater to determine a difference in ratings for items sampled by both members of each successive pair;

a module for designating at least one of the raters as a mentor and assigning a similarity function to the mentor based on the difference in ratings between that mentor and the selected user; and

a module for predicting the reaction of the selected user to the items not yet sampled by the selected user from a prediction function based on the similarity function, the at least one mentor'"'"'s rating of the items, and a previously determined prediction of the selected user'"'"'s reaction to the items.

13. A method of automatically predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, the reaction predictions being based on other items previously sampled by the selected user, comprising:

defining, for each item sampled by the selected user, a rating representing the reaction of the selected user to that item;

defining a set of raters including ones of the group of users, each rater in the set having a rating for one or more items sampled by the selected user, wherein at least one rater is an objective archetype having hypothetical user ratings for one or more items sampled by the selected user;

successively pairing the selected user with the raters to determine a difference in ratings for items sampled by both members of each successive pair;

designating at least one of the raters as a mentor and assigning a similarity function based on the difference in ratings between that mentor and the selected user; and

predicting the reaction of the user to the items not sampled by the selected user from a prediction function based on the similarity function, the mentor'"'"'s rating of the items, and a previously determined prediction of the user'"'"'s reaction to the items.

14. The method of

claim 13

, wherein the prediction function computes a weighted average of individual mentor ratings.

15. The method of

claim 13

, further comprising the step of:

computing a characteristic multidimensional value representing statistical properties of the ratings of each mentor and the selected user;

wherein the characteristic values are parameters to the prediction function.

16. The method of

claim 13

, wherein the similarity function computes an inverse of a weighted sum of normalized difference functions of ratings of items rated by that mentor and the selected user.

17. The method of

claim 13

, further comprising the step of:

forming a composite archetype having ratings reflecting ratings of a plurality of users in the group, wherein at least one rater is the composite archetype.

18. The method of

claim 17

, wherein the forming step comprises the steps of:

recording the ratings reflecting ratings of a plurality of users in the group as a mean and variance of the individual ratings; and

storing confidence values with the ratings reflecting the plurality of users in the group indicating a confidence that the ratings are accurate.

19. The method of

claim 13

, further comprising the step of:

storing the predicted reaction of the user to the items not sampled for use as a rater in subsequent predictions.

20. The method of

claim 13

, wherein each rating is a multidimensional value, with each dimension representing a different reaction type that led to the rating.

21. The method of

claim 13

, further comprising the step of:

if the predicted rating exceeds a predetermined threshold, notifying the selected user of the prediction.

22. The method of

claim 21

, wherein the notice is unsolicited.

23. The method of

claim 13

, wherein computer program steps for performing the method are encoded on a computer-readable medium.

24. The method of

claim 13

, wherein the method steps are performed on a computer system having a plurality of processors and wherein the defining, successively pairing, and designating steps are performed in parallel on ones of the plurality of processors.