Queue: First In First Out (FIFO): The first object into a queue is the first object to leave the queue, used by a queue.
Stack: Last In First Out (LIFO): The last object into a stack is the first object to leave the stack, used by a stack
Stack: First In Last Out (FILO): The first object or item in a stack is the last object or item to leave the stack.
Queue: Last In Last Out (LILO): The last object or item in a queue is the last object or item to leave the queue.
Also there would be random output, where items are just taken out of the queue at random
And then there is of course the holygrail which is "netflix's content recommendation engine queue"
Where somehow, based on user history, front weighted, the next link is chosen based on what the user has been interacting with, this is, very advanced stuff
Somewhat related, a sort algorithm could be added to the queues
The four main examples above output links based on "time or order they were added in"
But they could be sorted instead
Titles Alphanumerically ascending or descending
By domain name ascending or descending
By topic name ascending or descending (I would use a GPT LLM, feed title,url and page context text with prompt "what is the topic category of this web page" and use the result for topic tag and sorting)
I'm not sure which stack/queue mode would be most useful, but their algo are so simple and well known, I suggest just adding the most common
From a random internet page
https://everythingcomputerscience.com/discrete_mathematics/Stacks_and_Queues.html
Queue: First In First Out (FIFO): The first object into a queue is the first object to leave the queue, used by a queue. Stack: Last In First Out (LIFO): The last object into a stack is the first object to leave the stack, used by a stack Stack: First In Last Out (FILO): The first object or item in a stack is the last object or item to leave the stack. Queue: Last In Last Out (LILO): The last object or item in a queue is the last object or item to leave the queue.
Also there would be random output, where items are just taken out of the queue at random
And then there is of course the holygrail which is "netflix's content recommendation engine queue" Where somehow, based on user history, front weighted, the next link is chosen based on what the user has been interacting with, this is, very advanced stuff
Somewhat related, a sort algorithm could be added to the queues The four main examples above output links based on "time or order they were added in" But they could be sorted instead
Titles Alphanumerically ascending or descending By domain name ascending or descending By topic name ascending or descending (I would use a GPT LLM, feed title,url and page context text with prompt "what is the topic category of this web page" and use the result for topic tag and sorting)