Great paper! I really enjoyed reading it : D
I have one doubt though. It was mentioned in the paper, that for every session, you make 2 separate groups of songs, first session+ contains all consumed songs, second session- contains all the songs those were skipped. Then you take the average of all the songs in these 2 groups to create embeddings for session+ and session-, and then these embeddings are passed into the 2 LSTMs for various time-steps.
How do you handle the cases when, let's say, there are no skipped songs in a session? How do we find the embedding for such a session- which is empty?
Great paper! I really enjoyed reading it : D
I have one doubt though. It was mentioned in the paper, that for every session, you make 2 separate groups of songs, first session+ contains all consumed songs, second session- contains all the songs those were skipped. Then you take the average of all the songs in these 2 groups to create embeddings for session+ and session-, and then these embeddings are passed into the 2 LSTMs for various time-steps.
How do you handle the cases when, let's say, there are no skipped songs in a session? How do we find the embedding for such a session- which is empty?