Content recommender

• US 20090228918A1
• Filed: 03/05/2008
• Published: 09/10/2009
• Est. Priority Date: 03/05/2008
• Status: Abandoned Application

• Current Assignee: Amdocs Development Limited (Amdocs Ltd.), Amdocs Software Systems Limited (Amdocs Ltd.)
• Original Assignee: ChangingWorlds Ltd. (Amdocs Ltd.)
• Inventors: Rolff, Anders, Cotter, Paul, Bradley, Keith Joseph
• Application Number:
  US12/074,574
• Publication Number:
  US 20090228918A1
• Time in Patent Office: Days
• US Class Current: 725/34
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

A method for making an online content recommendation. In one embodiment, the method includes the steps of: providing a plurality of data source modules having data; providing a plurality of function modules, each function module adapted to be connected to at least one of the plurality of data source modules and other function modules; receiving a recommendation request; dynamically connecting at least one of the plurality of the data source modules and at least one of the plurality of function modules in response to the recommendation request; and generating the recommendation by using the connected at least one data source module and the at least one function module.

Claims

1. A method for making an online content recommendation comprising the steps of:

providing a plurality of data source modules having data;

providing a plurality of function modules, each function module adapted to be connected to at least one of the modules selected from the plurality of data source modules and other function modules;

receiving a recommendation request;

dynamically connecting at least one of the plurality of the data source modules and at least one of the plurality of function modules in response to the recommendation request; and

generating the recommendation by using the connected at least one data source module and the at least one function module.

2. The method of claim 1 further comprising the step of creating a recommendation specification, the recommendation specification defining at least one data source module and at least one function module for making the recommendation in response to the recommendation request.

3. The method of claim 1 further comprising the step of receiving user feedback on the recommendation.

4. The method of claim 1 wherein at least one of the plurality of data source modules includes a user profile.

5. The method of claim 1 wherein at least one of the plurality of data source modules includes an item profile.

6. The method of claim 1 wherein at least one of the plurality of function modules is a filter module.

7. The method of claim 1 wherein at least one of the plurality of function modules is a strategy module.

8. The method of claim 1 wherein at least one of the plurality of function modules is a hybrid strategy module.

9. The method of claim 1 wherein the recommendation is a personalized advertisement.

10. The method of claim 1 wherein the recommendation is a personalized search result.

11. The method of claim 1 further comprising the step of caching the recommendation with respect to the user and the user action.

12. A system for making an online content recommendation, the system comprising:

a plurality of data source modules having data;

a plurality of function modules, each function module adapted to be connected to at least one of the modules selected from the plurality of data source modules and other function modules;

a recommendation request receiving module adapted to receive a request for recommendations;

a recommendation factory adapted to dynamically assemble at least one of the plurality of function modules and at least one of the plurality of data source modules in response to the recommendation request, the recommendation factory in communication with the recommendation request receiving module; and

an online recommender for generating a recommendation using the assembled at least one function module and at least one data source module, the online recommender in communication with the recommendation factory.

13. The system of claim 12 further comprising a recommendation specification generator adapted to generate a recommendation specification in response to the request for recommendation, the recommendation specification generator in communication with the user input module.

14. The system of claim 12 further comprising a feedback handler for managing user feedbacks in response to the recommendation.

15. The system of claim 12 wherein at least one of the data source modules includes a user profile.

16. The system of claim 12 wherein at least one of the plurality of data source modules includes an item profile.

17. The system of claim 12 wherein at least one of the plurality of function modules is a filter module.

18. The system of claim 12 wherein at least one of the plurality of function modules is a strategy module.

19. The method of claim 12 wherein at least one of the plurality of function modules is a hybrid strategy module.

20. The system of claim 12 wherein the recommendation is a personalized advertisement.

21. The system of claim 12 wherein the recommendation is a personalized search result.

22. The system of claim 12 further comprising a caching module adapted to cache the recommendation with respect to the user and the user request.

23. The system of claim 12 where the recommendation request receiving module is adapted to receive search results from a search engine.

24. The system of claim 12 where the recommendation request receiving modules is adapted to communicate with an advertisement provider.