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  1. user_preference_modeling user_preference_modeling Public

    Multiple ways to model user preference in recommender systems

    Python 14 2

  2. causal_debiased_ranking causal_debiased_ranking Public

    We will show how to factorize and debias ranking to improve personalization and reduce popularity bias both on item side and to reduce the dominance of power users.

    Python 7 4

  3. two_tower_models two_tower_models Public

    We write sample code for two tower models for retrieval and add RLHF/RLAIF style alignment with a ranking model to make the retrieval more aligned with the ranking model on top

    Python 37 6

  4. kd_early_ranker kd_early_ranker Public

    Covers a couple of approaches to training an early ranker with knowledge distillation from final ranker

    Python 2 3

  5. pipelined_early_ranker pipelined_early_ranker Public

    Pipelined early ranker in a recommender system

    Python 3

  6. reward_maximizing_ranking reward_maximizing_ranking Public

    Adding REINFORCE based reward maximization to pointwise ranking

    Python 2 1