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Found 10 papers, 4 papers with code
ChatGPT for Conversational Recommendation: Refining Recommendations by Reprompting with Feedback
Recommendation algorithms have been pivotal in handling the overwhelming volume of online content.
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.
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
We present a theoretical framework to model the asymptotic evolution of the different components of a recommender system operating within a feedback loop setting, and derive theoretical bounds and convergence properties on quantifiable measures of the user discovery and blind spots.
Modeling and Counteracting Exposure Bias in Recommender Systems
Then we model the exposure that is borne from the interaction between the user and the recommender system and propose new debiasing strategies for these systems.
An Explainable Autoencoder For Collaborative Filtering Recommendation
In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style.
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.
Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
In this paper, we present a preliminary theoretical model and analysis of the mutual interaction between humans and algorithms, based on an iterated learning framework that is inspired from the study of human language evolution.
Explainable Restricted Boltzmann Machines for Collaborative Filtering
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations.
Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints
We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data.
