Neural Recommender Systems

[@farhanuddinfazaluddinkazi7198]

Thank you, loved the explaination, you covered quite a lot in very less time and also very clearly

[@dataroots]

Glad you liked it

[@Lelouchvv]

Thanks… 13:14 I have a question that why do you use precision, recall…. metrics (metrics for classification)? And how does model calculate that, because its not discrete value. I am a newbie

[@spicytuna08]

thanks. when you test, you are using data from training. i am referring to this line: long_test = wide_to_long( ) the parameter should be data['test'].. please correct me if i am wrong,

[@spicytuna08]

thanks. i see a problem with calling make_tf_dataset() just once for training. this function returns a size of 512 in tensor type. you are using this data just once for training. i think you need to put this into a loop. or make the batch size bigger. am i missing out in understanding?

[@efesencan8079]

My second question is, if interactions were not encoded as binary, but encoded as the actual ratings (explicit feedback rather implicit feedback), does your provided code still produce meaningful ncf_predcitons?

[@murilo-cunha]

I believe it should (it's been a while). The only thing you want to modify is to normalize the actual ratings between 0 and 1.

[@efesencan8079]

I did not really understand what these ncf_predictions means for the prediction. Does higher ncf_prediction value for specific (user_id,item_id) means they should be recommended to the user? Then, during the recommendation phase, for every (user_id,item_id) pair, should I recommend the item_id with the highest ncf value to that user?

[@murilo-cunha]

Yes, the highest predicted values that the user has not already seen/bought should be recommended. The ncf_predictions is basically the models' "guess" of whether you'd buy/watch by yourself (and we approximate "watched" = "liked").

[@efesencan8079]

@@murilo-cunha Thank you for the answers. Do you also have any recommendations to reduce the training time of the NCF model. I currently have 138k users and 1470 items. It takes more than days to finish the training process.

[@murilo-cunha]

@@efesencan8079 Hmm nothing in particular to this. You can always reduce the model size (layers, embedding size, etc.), scale your training up (get a more powerful machine - GPUs, etc.) or scale out (distributed training with SparkML or something). It's a bit hard to say without more specific info. Hope this helps!

[@chetouanethiziri3831]

hi @Efe Sencan can you give me the link to your dataset please, i am having trouble finding one, i am also working on social media users.

[@arvindchavan9759]

thanks.. I have question on userid information... is it possible to provide user related information as input to model?

[@murilo-cunha]

Yes you can. But then you are moving towards a more hybrid approach (as opposed to the collaborative filtering approach in the video).

[@fatmadehbi2946]

hello,thanks for this video if u can pls send me the code plz

[@murilo-cunha]

There are some links in the description. For google colab: colab.research.google.com/github/murilo-cunha/inteligencia-superficial/blob/master/_notebooks/2020-09-11-neural_collaborative_filter.ipynb

[@fatmadehbi2946]

@@murilo-cunha thanks a lot

[@spicytuna08]

thanks when you run a test, the results do not look good. for those with 'interaction' equal to 1, the prediction should be close to 1. but this is not the case.

[@Bawaromerali]

thanks , but the problem with these kind of videos is , you are talking to an expert guy who know all these things, but someone who does not know these things will not understand anything ! i hope in future videos be more detailed and slowly explain each steps not only read slides !

[@jagicyooo2007]

no one can hold your hands through everything; you need to do some research on ur own to get a feel for the context of this domain. I'd suggest you to do that first and then come back to re-watch the video.

[@Bawaromerali]

@@jagicyooo2007 Thanks for replay , i learnt and already built a recommender system and i understood these kind of videos is wasting time ! people should learn how to implement it not just short videos and highlights .