I have 100 sequences of the word (i.e., action for completing a task). Each of the sequences contains around 350 actions(115 unique actions but all the actions are not used in each sequence. Some of the actions may repeat). The dataset looks like as below:
Datapoint 1 Datapoint 2 ............. Datapoint 100
Add wall Add wall Add window
Edit wall Remove Roof Add wall
Add wall Add window Edit wall
....... ......... .........
........ ......... .........
Remove door Add door Remove door
My target is to predict the next design actions. However, when I used these sequences in the LSTM model, the prediction accuracy is not so high (35%). For this reason, I am thinking if I can use any embedding model. It is mention-worthy that actions in the sequences are correlated. It means each action has a certain relation with its previous actions and later actions. How can I represent these relationships using embedding? In short, I want to build my own embedding based on the sequence. If anyone help me to provide some reference, paper, it would be highly appreciated.