It seems that without a pretrained embedding input, the results become worse

[aimiyu]

Thanks for sharing your code.

I have run your code for a click-through rate prediction task in item recommendation scenario. In my setting, each item is seen as a token and users' earlier interactions on items can be seen as sentences. Then I use DiSAN to encode users' interaction sequence. If I replace the pre-trained item embedding with a random initializer embedding, the results evaluated by AUC become worse sharply.

SO I wonder whether DiSAN is suitable for training a token embedding meanwhile for sentence encoding? If not, then a good pre-trained embedding is in deed necessary for DiSAN to get the excellent performance.

For testify the difference before and after tuning the token embedding, I found that the change of embedding is subtle, especially when I calculate the top similar items with the embeddings.

[taoshen58]

Hi, @aimiyu
Have you tested the LSTM or CNN baselines on your own data? And what's the performance? Were there the same problems?

[alphadl]

@aimiyu Have you tried the suggestions by the author?