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I am working on two different architectures based on the LSTM model to predict the user's next action based on the previous actions. I am wondering, what is the best way to present the result? Is it okay to present only the prediction accuracy? Or Should I use other metrics? I found a paper using top_K_accuracy whereas on a different paper I found AUC or ROC. Overall, I would like to know what is the state of the art of presenting prediction accuracy based on the LSTM model.

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There really is nothing special about LSTMs when it comes to classification and metrics. So your question should be what metrics are good for multi-class classification. Both accuracy and auc works. Another metric that is good here is Matthew's Correlation Coefficient (MCC) which is similar to F1 score for a binary problem.

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