ITEM-BASED RECOMMENDATION ENGINE FOR RECOMMENDING A HIGHLY-ASSOCIATED ITEM

• US 20140365456A1
• Filed: 08/27/2012
• Published: 12/11/2014
• Est. Priority Date: 08/26/2011
• Status: Abandoned Application

• Current Assignee: NHN BUGS Corp.
• Original Assignee: NHN BUGS Corp.
• Inventors: Lee, Min Jae
• Application Number:
  US14/241,193
• Publication Number:
  US 20140365456A1
• Time in Patent Office: Days
• US Class Current: 707/706
• CPC Class Codes: G06F 16/3347 using vector based model
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Disclosed relates to a recommendation engine. The recommendation engine includes a query generation module configured to store a plurality of item vectors as a plurality of documents, configured to search for a reference document associated with the reference item among the plurality of the documents to extract a reference item vector and configured to generate a query including at least one user best associated with the extracted reference item vector if successful, each of the plurality of the item vectors corresponding to an element including a user-preference pair and a search module configured to calculate a correlation between the extracted reference item vector and each of the plurality of the item vectors in the plurality of the documents based on the generated query to provide the at least one recommendation item.

Claims

1. A recommendation engine searching for at least one recommendation item associated with a reference item, the reference item being selected by a query user, the recommendation engine comprising:

a query generation module configured to store a plurality of item vectors as a plurality of documents, configured to search for a reference document associated with the reference item among the plurality of the documents to extract a reference item vector and configured to generate a query including at least one user best associated with the extracted reference item vector if successful, each of the plurality of the item vectors corresponding to an element including a user-preference pair; and

a search module configured to calculate a correlation between the extracted reference item vector and each of the plurality of the item vectors in the plurality of the documents based on the generated query to provide the at least one recommendation item.

2. The recommendation engine of claim 1, wherein the search module calculates a correlation between a preference of the at least one user and a preference of at least one user in each of the plurality of the item vectors.

3. The recommendation engine of claim 2, wherein the correlation is calculated by using a Pearson Coefficient.

4. The recommendation engine of claim 3, wherein the query defines each of the at least one user as a query element and the query element includes at least a corresponding preference as a boost and a corresponding user as a terminology.

5. The recommendation engine of claim 4, wherein the search module searches for at least one item vector having best ranking among the plurality of the item vectors based on the query element.

6. The recommendation engine of claim 5, wherein the ranking is calculated based on the boost and the Pearson Coefficient.

7. The recommendation engine of claim 1, wherein the query defines each of the at least one user as a query element and the query element includes at least a constant independent of a corresponding preference as a boost and a corresponding user as a terminology.

8. The recommendation engine of claim 1, further comprising:

a popular recommendation module configured to determine, as the at least one recommendation item, at least one item recent frequently searched in a current time window and independent of the reference item if unsuccessful.

9. The recommendation engine of claim 1, wherein the query generation module generates a query including the query user independent of the reference item if unsuccessful.

10. The recommendation engine of claim 9, wherein the search module searches the plurality of the item vectors for the query user to determine at least one item having a best preference as the at least recommendation item.

11. The recommendation engine of claim 1, wherein a structure of the query includes a following tree structure.

12. An item recommendation method performed by a recommendation engine searching for at least one recommendation item associated with a reference item, the reference item being selected by a query user, the method comprising:

storing a plurality of item vectors as a plurality of documents and searching for a reference document associated with the reference item among the plurality of the documents to extract a reference item vector;

generating a query including at least one user best associated with the extracted reference item vector if successful; and

calculating a correlation between the extracted reference item vector and each of the plurality of the item vectors in the plurality of the documents based on the generated query to provide the at least one recommendation item

wherein each of the plurality of the item vectors corresponds to an element including a user-preference pair.

13. The item recommendation method of claim 12, further comprising:

determining, as the at least one recommendation item, at least one item recent frequently searched in a current time window and independent of the reference item if unsuccessful.

14. The item recommendation method of claim 12, further comprising:

generating a query including the query user independent of the reference item if unsuccessful; and

searching the plurality of the item vectors for the query user to determine at least one item having a best preference as the at least recommendation item.