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Found 25 papers, 19 papers with code
General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
However, we have discovered that this aggregation mechanism comes with a drawback, which amplifies biases present in the interaction graph.
Towards Goal-oriented Intelligent Tutoring Systems in Online Education
In this work, we investigate a new task, named Goal-oriented Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
Large Language Model Can Interpret Latent Space of Sequential Recommender
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items.
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods.
Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
Our results show that a subgraph-level tokenizer and a sufficiently expressive decoder with remask decoding have a large impact on the encoder's representation learning.
ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction
Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process.
Robust Collaborative Filtering to Popularity Distribution Shift
The popularity shortcut tricks are good for in-distribution (ID) performance but poorly generalized to out-of-distribution (OOD) data, i. e., when popularity distribution of test data shifts w. r. t.
On Generative Agents in Recommendation
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development.
Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation
To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features.
Redundancy-aware Transformer for Video Question Answering
This paper identifies two kinds of redundancy in the current VideoQA paradigm.
Discovering Dynamic Causal Space for DAG Structure Learning
Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.
Invariant Collaborative Filtering to Popularity Distribution Shift
Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios.
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences.
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds.
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively.
Differentiable Invariant Causal Discovery
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
Reinforced Causal Explainer for Graph Neural Networks
Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.
Discovering Invariant Rationales for Graph Neural Networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.
Deconfounding to Explanation Evaluation in Graph Neural Networks
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
Towards Multi-Grained Explainability for Graph Neural Networks
A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).
Causal Screening to Interpret Graph Neural Networks
In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.
Disentangled Graph Collaborative Filtering
Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.
