Problem of Large action spaces in interest evolution

[riskr12]

I am trying to use RecSim(decompose_q with interest evolution) for training of recommendation.
My action space( video ) is quite large and one-hot encoding will not work as we do in interest_evolution environment.
Can you please suggest some other methodology by which we can simulate the environment.

[cwhsu-google]

Hi, Sorry for the late reply. You probably need an embedding layer mapping from videos to action encoding and more thoughts on how we enumerate action/slate space efficiently. Thanks for using RecSim.

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On Wed, Mar 9, 2022 at 3:28 AM riskr12 ***@***.***> wrote: I am trying to use RecSim(decompose_q with interest evolution) for training of recommendation. My action space( video ) is quite large and one-hot encoding will not work as we do in interest_evolution environment. Can you please suggest some other methodology by which we can simulate the environment. — Reply to this email directly, view it on GitHub <#23>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ALVSYDQZRF4PF2J5D6GMWDLU7CDPLANCNFSM5QJH767Q> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>