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Found 15 papers, 4 papers with code
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e. g., products, jobs, news, video) and their providers.
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets
In addition, ensuring that recommendation opportunities do not disproportionately favor popular users is essential for the total number of matches and for fairness among users.
Safe Collaborative Filtering
Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset.
A Critical Reexamination of Intra-List Distance and Dispersion
Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs.
Curse of "Low" Dimensionality in Recommender Systems
Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness.
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images.
Fair Matrix Factorisation for Large-Scale Recommender Systems
However, the intrinsic nature of fairness destroys the separability of optimisation subproblems for users and items, which is an essential property of conventional scalable algorithms, such as implicit alternating least squares (iALS).
DM$^2$S$^2$: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce.
Matching Theory-based Recommender Systems in Online Dating
Online dating platforms provide people with the opportunity to find a partner.
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval
First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-$K$ ranked list by treating the list as a set.
Optimal Correction Cost for Object Detection Evaluation
OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy.
Scalable Personalised Item Ranking through Parametric Density Estimation
However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e. g. IRGAN) cannot achieve practical efficiency due to the training of an extra model.
Density-Ratio Based Personalised Ranking from Implicit Feedback
Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones.
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items.
Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering
Embedding based approaches are shown to be effective for solving simple Question Answering (QA) problems in recent works.
