View source for 17698667. METHOD AND APPARATUS FOR MODELING USER PREFERENCES simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

=METHOD AND APPARATUS FOR MODELING USER PREFERENCES=


==Organization Name==

[[:Category:SAMSUNG ELECTRONICS CO., LTD.|SAMSUNG ELECTRONICS CO., LTD.]]


[[Category:SAMSUNG ELECTRONICS CO., LTD.]]

==Inventor(s)==

[[:Category:Jehun Jeon of Suwon-si (KR)|Jehun Jeon of Suwon-si (KR)]][[Category:Jehun Jeon of Suwon-si (KR)]]

[[:Category:Jangsu Lee of Seoul (KR)|Jangsu Lee of Seoul (KR)]][[Category:Jangsu Lee of Seoul (KR)]]

[[:Category:Inkyu Choi of Seoul (KR)|Inkyu Choi of Seoul (KR)]][[Category:Inkyu Choi of Seoul (KR)]]

[[:Category:GyuBum Han of Suwon-si (KR)|GyuBum Han of Suwon-si (KR)]][[Category:GyuBum Han of Suwon-si (KR)]]

==METHOD AND APPARATUS FOR MODELING USER PREFERENCES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17698667 titled 'METHOD AND APPARATUS FOR MODELING USER PREFERENCES

==Simplified Explanation==

The patent application describes a method for modeling user preferences. Here is a simplified explanation of the abstract:

* The method involves receiving preference scores for different items.
* The user selects an item from a list of items.
* The preference scores of the items in two separate lists are decayed based on a time decay rate and two different parameters.
* If the selected item is in the first list, the preference scores of the items in both lists are decayed.
* If the selected item is in the second list, only the preference scores of the items in the second list are decayed.
* The preference score of the selected item is increased.
* The first list can include some items based on their preference scores, and it is different from the second list.

Potential applications of this technology:

* Personalized recommendation systems: The method can be used to model user preferences and improve recommendation algorithms by considering time decay and different parameters.
* E-commerce platforms: The method can help in providing personalized product recommendations to users based on their preferences and recent interactions.
* Content streaming services: By modeling user preferences and adjusting preference scores over time, the method can enhance content recommendations for users.

Problems solved by this technology:

* Overcoming the issue of static preference scores: By decaying preference scores based on time and parameters, the method accounts for changes in user preferences over time.
* Addressing the challenge of differentiating between lists: The method distinguishes between two lists and applies different decay rates and parameters based on the selected item's inclusion in each list.

Benefits of this technology:

* Improved user experience: By considering time decay and adjusting preference scores, the method can provide more relevant and up-to-date recommendations to users.
* Increased user engagement: Personalized recommendations based on user preferences can enhance user engagement and satisfaction.
* Enhanced recommendation accuracy: By modeling user preferences and adjusting scores, the method can improve the accuracy of recommendation algorithms.


==Original Abstract Submitted==

A user preference modeling method including receiving preference scores corresponding to items, receiving an input for selecting an item from among the items, decaying a preference score corresponding to one or more items from among the items included in a first list and a second list based on a time decay rate and a first parameter, in response to the selected item being included in the first list, decaying the preference score corresponding to the one or more items comp included in the first list and the second list based on a time decay rate and a second parameter, in response to the selected item being included in the second list, and increasing a preference score corresponding to the selected item, wherein the first list may include one or more of the plurality of items based on the preference scores, and wherein the first list is different from the second list.



[[Category:G06F16/9535]]