Recommendation system for providing personalized and mixed content on a user interface based on content and user similarity

1. A recommendation system comprising:

at least one memory, wherein the memory stores instructions; and

at least one processor configured to execute the instructions and cause the recommendation system to perform,

selecting a subset of content item identifiers from a first list based on corresponding similarity scores between the content item identifiers of the first list and a viewing history associated with a first user identifier, the content item identifiers including at least one content item identifier similar to a set of viewed content items of the first user identifier and at least one content item identifier based on respective viewing histories of a set of user identifiers similar to the first user identifier; and

transmitting the subset of content item identifiers for display of corresponding content items via a web portal on a user interface of a first user device.

2. The recommendation system of claim 1 , wherein the processor is further configured to execute instructions to cause the recommendation system to perform generating a set of similarity scores for a first content item by comparing the first content item to a set of content items stored in a content database.

3. The recommendation system of claim 2 , wherein the first content item is compared to the set of content items in response to the first content item being uploaded to the content database.

4. The recommendation system of claim 2 , wherein the set of similarity scores includes a similarity score comparing the first content item and each content item of the set of content items.

5. The recommendation system of claim 1 , wherein the first list is stored in a recommendation database.

6. The recommendation system of claim 1 , wherein the processor is further configured to execute instructions to cause the recommendation system to perform excluding content item identifiers from the subset of content item identifiers in response to content item identifiers of the first list matching a previously viewed content item identifier included in the viewing history associated with a first user identifier.

7. The recommendation system of claim 1 , further comprising a similar user database configured to store user parameters for a plurality of users.

8. The recommendation system of claim 7 , wherein the processor is further configured to execute instructions to cause the recommendation system to perform categorizing the plurality of users into a set of groups based on the corresponding user parameters stored in the similar user database and storing the set of groups in the user similarity database.

9. The recommendation system of claim 7 , wherein the user parameters of the similar user database include (i) portfolio structure, (ii) trading activity, (iii) platform usage, and (iv) demographic information.

10. The recommendation system of claim 7 , wherein the processor is further configured to execute instructions to cause the recommendation system to perform identifying the set of similar user identifiers by selecting the set of similar user identifiers from a first group of the set of groups in response to the first user identifier being categorized in the first group.

11. The recommendation system of claim 10 , wherein the processor is further configured to execute instructions to cause the recommendation system to perform selecting a threshold number of identifiers as the set of similar user identifiers.

12. A recommendation method comprising:

selecting a subset of content item identifiers from a first list based on corresponding similarity scores between the content item identifiers of the first list and a viewing history associated with a first user identifier, the content item identifiers including at least one content item identifier similar to a set of viewed content items of the first user identifier and at least one content item identifier based on respective viewing histories of a set of user identifiers similar to the first user identifier; and

transmitting the subset of content item identifiers for display of corresponding content items via a web portal on a user interface of a first user device.

13. The recommendation method of claim 12 , further comprising generating a set of similarity scores for a first content item by comparing the first content item to a set of content items stored in a content database.

14. The recommendation method of claim 13 , further comprising comparing the first content item to the set of content items in response to the first content item being uploaded to the content database.

15. The recommendation method of claim 13 , wherein the set of similarity scores includes a similarity score comparing the first content item and each content item of the set of content items.

16. The recommendation method of claim 12 , wherein the first list is stored in a recommendation database.

17. The recommendation method of claim 12 , further comprising excluding content item identifiers from the subset of content item identifiers in response to content item identifiers of the first list matching a previously viewed content item identifier included in the viewing history associated with the first user identifier.

18. The recommendation method of claim 12 , further comprising:

categorizing a plurality of users into a set of groups based on corresponding user parameters, wherein a similar user database stores user parameters for the plurality of users; and

storing the set of groups in the user similarity database.

19. The recommendation method of claim 18 , wherein identifying the set of similar user identifiers includes selecting the set of similar user identifiers from a first group of the set of groups in response to the first user identifier being categorized in the first group.

20. The recommendation method of claim 19 , further comprising selecting a threshold number of identifiers as the set of similar user identifiers.