Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
- URL: http://arxiv.org/abs/2209.11679v2
- Date: Thu, 27 Jul 2023 15:32:45 GMT
- Title: Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
- Authors: Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, Xiangnan He
- Abstract summary: We propose a new Aleatoric Uncertainty-aware Recommendation (AUR) framework.
AUR consists of a new uncertainty estimator along with a normal recommender model.
As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty.
- Score: 59.500347564280204
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Historical interactions are the default choice for recommender model
training, which typically exhibit high sparsity, i.e., most user-item pairs are
unobserved missing data. A standard choice is treating the missing data as
negative training samples and estimating interaction likelihood between
user-item pairs along with the observed interactions. In this way, some
potential interactions are inevitably mislabeled during training, which will
hurt the model fidelity, hindering the model to recall the mislabeled items,
especially the long-tail ones. In this work, we investigate the mislabeling
issue from a new perspective of aleatoric uncertainty, which describes the
inherent randomness of missing data. The randomness pushes us to go beyond
merely the interaction likelihood and embrace aleatoric uncertainty modeling.
Towards this end, we propose a new Aleatoric Uncertainty-aware Recommendation
(AUR) framework that consists of a new uncertainty estimator along with a
normal recommender model. According to the theory of aleatoric uncertainty, we
derive a new recommendation objective to learn the estimator. As the chance of
mislabeling reflects the potential of a pair, AUR makes recommendations
according to the uncertainty, which is demonstrated to improve the
recommendation performance of less popular items without sacrificing the
overall performance. We instantiate AUR on three representative recommender
models: Matrix Factorization (MF), LightGCN, and VAE from mainstream model
architectures. Extensive results on two real-world datasets validate the
effectiveness of AUR w.r.t. better recommendation results, especially on
long-tail items.
Related papers
- Debiased Recommendation with Noisy Feedback [41.38490962524047]
We study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data.
First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios.
arXiv Detail & Related papers (2024-06-24T23:42:18Z) - Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
In safety-critical applications of machine learning, it is often desirable for a model to be conservative.
We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
arXiv Detail & Related papers (2023-06-08T07:05:36Z) - R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility
Across Random User Intents [14.455036827804541]
Large language models show impressive results at predicting structured text such as code, but also commonly introduce errors and hallucinations in their output.
We propose Randomized Utility-driven Synthesis of Uncertain REgions (R-U-SURE)
R-U-SURE is an approach for building uncertainty-aware suggestions based on a decision-theoretic model of goal-conditioned utility.
arXiv Detail & Related papers (2023-03-01T18:46:40Z) - Debiasing Learning for Membership Inference Attacks Against Recommender
Systems [79.48353547307887]
Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations.
We investigate privacy threats faced by recommender systems through the lens of membership inference.
We propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components.
arXiv Detail & Related papers (2022-06-24T17:57:34Z) - Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
We develop a new learning paradigm named Cross Pairwise Ranking (CPR)
CPR achieves unbiased recommendation without knowing the exposure mechanism.
We prove in theory that this way offsets the influence of user/item propensity on the learning.
arXiv Detail & Related papers (2022-04-26T09:20:27Z) - WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation
Models [24.455665093145818]
We propose a novel model-agnostic training approach called WSLRec, which adopts a three-stage framework: pre-training, top-$k$ mining, intrinsic and fine-tuning.
WSLRec resolves the incompleteness problem by pre-training models on extra weak supervisions from model-free methods like BR and ItemCF, while resolving the inaccuracy problem by leveraging the top-$k$ mining to screen out reliable user-item relevance from weak supervisions for fine-tuning.
arXiv Detail & Related papers (2022-02-28T08:55:12Z) - Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items.
We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues.
We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
arXiv Detail & Related papers (2022-01-16T12:38:45Z) - Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate
Estimation [29.27760413892272]
Post-click conversion, as a strong signal indicating the user preference, is salutary for building recommender systems.
Currently, most existing methods utilize counterfactual learning to debias recommender systems.
We propose a novel double learning approach for the MRDR estimator, which can convert the error imputation into the general CVR estimation.
arXiv Detail & Related papers (2021-05-28T06:59:49Z) - Latent Unexpected Recommendations [89.2011481379093]
We propose to model unexpectedness in the latent space of user and item embeddings, which allows to capture hidden and complex relations between new recommendations and historic purchases.
In addition, we develop a novel Latent Closure (LC) method to construct hybrid utility function and provide unexpected recommendations based on the proposed model.
arXiv Detail & Related papers (2020-07-27T02:39:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.