Recommendation system, method and non-transitory computer readable storage medium for storing thereof

• US 9,659,302 B2
• Filed: 11/25/2013
• Issued: 05/23/2017
• Est. Priority Date: 10/15/2013
• Status: Active Grant

• Current Assignee: Institute For Information Industry
• Original Assignee: Institute For Information Industry
• Inventors: Shih, Meng-Jung, Chen, Ping-I, Wang, Tai-Chun, Shae, Zon-Yin
• Primary Examiner(s): Rostami, Mohammad S
• Assistant Examiner(s): Abraham, Ahmed
• Application Number:
  US14/088,534
• Publication Number:
  US 20150106374A1
• Time in Patent Office: 1,275 Days
• Field of Search: 707737, 707738, 707739
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A recommendation method includes providing an ontology database, in which the ontology database includes a plurality of entities, and the entities are arranged in an ontology hierarchy structure with N hierarchy levels; storing a plurality of j^th level user data respectively corresponding to a plurality of users; generating a plurality of k^th level user data according to the j^th level user data respectively; clustering the k^th level user data; and recommending the entities in the ontology database to the users according to a clustering result.

Claims

1. A recommendation method comprising:

providing an ontology database comprising an ontology hierarchy structure with N hierarchy levels, wherein N is an integer, and each of the hierarchy levels comprises at least one entity;

storing, through the ontology database, a plurality of j^th user data respectively corresponding to a plurality of users, wherein each of the j^th user data records at least one j^th entity of the entities on a j^th hierarchy level of the ontology hierarchy structure;

generating a plurality of k^th user data corresponding to the users according to the j^th user data respectively, wherein each of the k^th user data records at least one k^th entity of the entities on a k^th hierarchy level of the ontology hierarchy structure;

clustering the k^th user data; and

recommending the entities in the ontology database to the users according to the clustering result,

wherein the step of generating the k^th user data corresponding to the users comprises;

calculating a sparsity of the j^th user data; and

mapping the j^th entity recorded in each of the j^th user data to at least one of the k^th entity recorded in each of the k^th user data according to the sparsity of the j^th user data;

wherein a first calculating value is equal to a product of a quantity of the users and a quantity of the entities on the j^th hierarchy level, a second calculating value is equal to a quantity of a sum of the j^th entity recorded by each of the j^th user data divided by the first calculating value, and the sparsity of the j^th user data is equal to 1 subtracted by the second calculating value.

2. The recommendation method as claimed in claim 1, wherein the step of generating the k^th user data corresponding to the users comprises:

mapping the j^th entity recorded in each of the j^th user data to at least one of the entities on the k^th hierarchy level according to the ontology hierarchy structure, to serve as the k^th entity recorded in each of the k^th user data.

3. The recommendation method as claimed in claim 1, wherein the step of mapping the j^th entity recorded in each of the j^th user data to at least one of the k^th entity recorded in each of the k^th user data comprises:

determining whether the sparsity of the j^th user data is greater than a j^th threshold; and

mapping the j^th entity recorded in each of the j^th user data to at least one of the k^th entity recorded in each of the k^th user data in a case that the sparsity of the j^th user data is greater than the j^th threshold.

4. The recommendation method as claimed in claim 3, wherein the step of mapping the j^th entity recorded in each of the j^th user data to at least one of the k^th entity recorded in each of the k^th user data further comprises:

serving the j^th user data as the k^th user data in a case that the sparsity of the j^th user data is not greater than the j^th threshold.

5. The recommendation method as claimed in claim 1, wherein the step of recommending the entities in the ontology database to the users according to the clustering result comprises:

searching a k^th frequent entity from the k^th entity recorded by each of the k^th user data in a cluster;

searching a j^th frequent entity from the entities on the j^th hierarchy level according to the k^th frequent entity; and

recommending the j^th frequent entity to one of the users corresponding to the k^th user data in the cluster.

6. The recommendation method as claimed in claim 5, wherein the step of searching the j^th frequent entity from the entities on the j^th hierarchy level according to the k^th frequent entity comprises:

determining whether the k^th frequent entity is one of the entities on the j^th hierarchy level;

mapping the k^th frequent entity to corresponding entities of the entities on a k−

1^th hierarchy level of the ontology hierarchy structure in a case that the k^th frequent entity is not one of the entities on the j^th hierarchy level; and

searching, through a frequent pattern mining algorithm, a k−

1^th frequent entity from the corresponding entities on the k−

1^th hierarchy level mapped by the k^th frequent entity.

7. The recommendation method as claimed in claim 6, wherein the step of searching the j^th frequent entity from the entities on the j^th hierarchy level according to the k^th frequent entity further comprises:

serving the k^th frequent entity as the j^th frequent entity in a case that the k^th frequent entity is one of the entities on the j^th hierarchy level.

8. A recommendation system comprising:

a storage module configured to store an ontology database comprising an ontology hierarchy structure with N hierarchy levels, N is an integer, each of the hierarchy levels comprises at least one entity, the ontology database is configured to store a plurality of j^th user data respectively corresponding to a plurality of users, and each of the j^th data records at least one j^th entity of the entities on a j^th hierarchy level of the ontology hierarchy structure;

a converting module configured to generate a plurality of k^th user data corresponding to the users according to the j data respectively, wherein each of the k^th user data records at least one k^th entity of the entities on a k^th hierarchy level of the ontology hierarchy structure;

a clustering module configured to cluster the k^th user data; and

a recommendation module configured to recommend the entities in the ontology database to the users according to the clustering result;

wherein the converting module is configured to calculate a sparsity of the j^th user data, and map the j^th entity recorded in each of the j^th user data to at least one of the k^th entity recorded in each of the k^th user data according to the sparsity of the j^th user data;

wherein a first calculating value is equal to a product of a quantity of the users and a quantity of the entities on the j^th hierarchy level, a second calculating value is equal to a quantity of a sum of the j^th entity recorded by each of the j^th user data divided by the first calculating value, and the sparsity of the j^th user data is equal to 1 subtracted by the second calculating value.

9. The recommendation system as claimed in claim 8, wherein the converting module is configured to map the j^th entity recorded in each of the j^th user data to at least one of the entities on the k^th hierarchy level according to the ontology hierarchy structure, to serve as the k^th entity recorded in each of the k^th user data.

10. The recommendation system as claimed in claim 8, wherein the converting module is configured to determine whether the sparsity of the j^th user data is greater than a j^th threshold, and map the j^th entity recorded in each of the j^th user data to at least one of the k^th entity recorded in each of the k^th user data in a case that the sparsity of the j^th user data is greater than the j^th threshold.

11. The recommendation system as claimed in claim 10, wherein the converting module is configured to serve the j^th user data as the k^th user data in a case that the sparsity of the j^th user data is not greater than the j^th threshold.

12. The recommendation system as claimed in claim 8, wherein the recommendation module is configured to search a k^th frequent entity from the k^th entity recorded by each of the k^th user data in a cluster, search a j^th frequent entity from the entities on the j^th hierarchy level according to the k^th frequent entity, and recommend the j^th frequent entity to one of the users corresponding to the k^th user data in the one of the cluster.

13. The recommendation system as claimed in claim 12, wherein the recommendation module is configured to determine whether the k^th frequent entity is one of the entities on the j^th hierarchy level, map the k^th frequent entity to corresponding entities of the entities on a k−

1^th hierarchy level of the ontology hierarchy structure in a case that the k^th frequent entity is not one of the entities on the j^th hierarchy level, and search, through a frequent pattern mining algorithm, a k−

1^th frequent entity from the corresponding entities on the k−

1^th hierarchy level mapped by the k^th frequent entity.

14. The recommendation system as claimed in claim 13, wherein the recommendation module is configured to serve the k^th frequent entity as the j^th frequent entity in a case that the k^th frequent entity is one of the entities on the j^th hierarchy level.

15. A non-transitory computer readable storage medium for storing a computer program configured to execute a recommendation method applied to a recommendation system, wherein the recommendation method comprises:

providing an ontology database, wherein the ontology database comprises a plurality of entities, the entities are arranged in an ontology hierarchy structure with N hierarchy levels, and N is an integer;

storing, through the ontology database, a plurality of j^th user data respectively corresponding to a plurality of users, wherein each of the j^th user data records at least one j^th entity of the entities on the j^th hierarchy level;

generating a plurality of k^th user data corresponding to the users according to the j^th user data respectively, wherein each of the k^th user data records at least one k^th entity of the entities on a k^th hierarchy level of the ontology hierarchy structure;

clustering the k^th user data; and

recommending the entities in the ontology database to the users according to the clustering result;

wherein the step of generating the k^th user data corresponding to the users comprises;

calculating a sparsity of the j^th user data; and

mapping the j^th entity recorded in each of the j^th user data to at least one of the k^th entity recorded in each of the k^th user data according to the sparsity of the j^th user data;

wherein a first calculating value is equal to a product of a quantity of the users and a quantity of the entities on the j^th hierarchy level, a second calculating value is equal to a quantity of a sum of the j^th entity recorded by each of the j^th user data divided by the first calculating value, and the sparsity of the j^th user data is equal to 1 subtracted by the second calculating value.

16. The non-transitory computer readable storage medium as claimed in claim 15, wherein the step of generating the k^th user data corresponding to the users comprises:

mapping the j^th entity recorded in each of the j^th user data to at least one of the entities on the k^th hierarchy level according to the ontology hierarchy structure, to serve as the k^th entity recorded in each of the k^th user data.