Search Results for author:
Found 9 papers, 2 papers with code
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms.
Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
Specifically, we consider the influence of confounders, disentangle them from user preferences in the latent space, and employ causal graphs to model their interdependencies without specific labels.
Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
Recommender models aim to capture user preferences from historical feedback and then predict user-specific feedback on candidate items.
Deep Generative Imputation Model for Missing Not At Random Data
Existing statistical methods model the MNAR mechanism by different decomposition of the joint distribution of the complete data and the missing mask.
TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation
In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications.
Detect Professional Malicious User with Metric Learning in Recommender Systems
2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem.
A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems
On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage.
Symmetric Transformer-based Network for Unsupervised Image Registration
Medical image registration is a fundamental and critical task in medical image analysis.
Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems
Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them boring and unsatisfied.
