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I'm trying to create a pairwise learning model where user will be provided with 2 items (say $A$ and $B$) from which the user will provide the preference order ($A$ over $B$ or vice versa). Let's say each item is associated with $N$ features and every item is mapped to $N$-dimensional space. It should be an online model where the next set of item pairs will depend on the current user feedback. My goal is to map users and items to a common embedding space.






machine-learning reinforcement-learning






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    I'm trying to create a pairwise learning model where user will be provided with 2 items (say $A$ and $B$) from which the user will provide the preference order ($A$ over $B$ or vice versa). Let's say each item is associated with $N$ features and every item is mapped to $N$-dimensional space. It should be an online model where the next set of item pairs will depend on the current user feedback. My goal is to map users and items to a common embedding space.






    machine-learning reinforcement-learning






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      $begingroup$


      I'm trying to create a pairwise learning model where user will be provided with 2 items (say $A$ and $B$) from which the user will provide the preference order ($A$ over $B$ or vice versa). Let's say each item is associated with $N$ features and every item is mapped to $N$-dimensional space. It should be an online model where the next set of item pairs will depend on the current user feedback. My goal is to map users and items to a common embedding space.






      machine-learning reinforcement-learning






      share









      $endgroup$




      I'm trying to create a pairwise learning model where user will be provided with 2 items (say $A$ and $B$) from which the user will provide the preference order ($A$ over $B$ or vice versa). Let's say each item is associated with $N$ features and every item is mapped to $N$-dimensional space. It should be an online model where the next set of item pairs will depend on the current user feedback. My goal is to map users and items to a common embedding space.






      machine-learning reinforcement-learning




      machine-learning reinforcement-learning





      share












      share










      share



      share










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      lone_riderlone_rider

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