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Found 3 papers, 3 papers with code
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers
The most common approach to mitigating exposure bias in recommendation has been Inverse Propensity Scoring (IPS), which consists of down-weighting the interacted predictions in the loss function in proportion to their propensities of exposure, yielding a theoretically unbiased learning.
Debiased Explainable Pairwise Ranking from Implicit Feedback
In this work, we first propose a novel explainable loss function and a corresponding Matrix Factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations.
SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures.
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