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Found 92 papers, 58 papers with code
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning.
Exact and Efficient Unlearning for Large Language Model-based Recommendation
The evolving paradigm of Large Language Model-based Recom- mendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommenda- tion data.
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients
Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information.
Think Twice Before Assure: Confidence Estimation for Large Language Models through Reflection on Multiple Answers
Confidence estimation aiming to evaluate output trustability is crucial for the application of large language models (LLM), especially the black-box ones.
Proactive Recommendation with Iterative Preference Guidance
However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization.
The 2nd Workshop on Recommendation with Generative Models
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations.
Uplift Modeling for Target User Attacks on Recommender Systems
UBA estimates the treatment effect on each target user and optimizes the allocation of fake user budgets to maximize the attack performance.
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Compared to AUC, LLPAUC considers only the partial area under the ROC curve in the Lower-Left corner to push the optimization focus on Top-K. We provide theoretical validation of the correlation between LLPAUC and Top-K ranking metrics and demonstrate its robustness to noisy user feedback.
Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning
To achieve this, we propose a Bi-level Learnable LLM Planner framework, which combines macro-learning and micro-learning through a hierarchical mechanism.
Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Additionally, we prospect the evolution of Rec4Agentverse and conceptualize it into three stages based on the enhancement of the interaction and information exchange among Agent Items, Agent Recommender, and the user.
DiFashion: Towards Personalized Outfit Generation and Recommendation
The evolution of Outfit Recommendation (OR) in the realm of fashion has progressed through two distinct phases: Pre-defined Outfit Recommendation and Personalized Outfit Composition.
Item-side Fairness of Large Language Model-based Recommendation System
Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS.
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
We conduct a theoretical analysis of the learning process for the weights in the linear component, revealing how group-wise properties of training data influence them.
Data-efficient Fine-tuning for LLM-based Recommendation
To pursue the two objectives, we propose a novel data pruning method based on two scores, i. e., influence score and effort score, to efficiently identify the influential samples.
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e. g. SEC filings), where discrete reasoning capabilities are often required.
Plug-In Diffusion Model for Embedding Denoising in Recommendation System
In the realm of recommender systems, handling noisy implicit feedback is a prevalent challenge.
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Rather than directly dismissing the role of incremental learning, we ascribe this lack of anticipated performance improvement to the mismatch between the LLM4Recarchitecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendation, hampering its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context.
LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
Through meta-learning techniques, LabelCraft effectively addresses the bi-level optimization hurdle posed by the recommender and labeling models, enabling the automatic acquisition of intricate label generation mechanisms. Extensive experiments on real-world datasets corroborate LabelCraft's excellence across varied operational metrics, encompassing usage time, user engagement, and retention.
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
They learn a feature extractor on warm-start items to align feature representations with interactions, and then leverage the feature extractor to extract the feature representations of cold-start items for interaction prediction.
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice.
CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation.
Attack Prompt Generation for Red Teaming and Defending Large Language Models
Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks.
A Multi-facet Paradigm to Bridge Large Language Model and Recommendation
To combat these issues, we propose a novel multi-facet paradigm, namely TransRec, to bridge the LLMs to recommendation.
RecAD: Towards A Unified Library for Recommender Attack and Defense
Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments.
Label Denoising through Cross-Model Agreement
We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendations.
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
Recommendation Unlearning via Influence Function
In this work, we propose an Influence Function-based Recommendation Unlearning (IFRU) framework, which efficiently updates the model without retraining by estimating the influence of the unusable data on the model via the influence function.
Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
With the proliferation of short video applications, the significance of short video recommendations has vastly increased.
Robust Prompt Optimization for Large Language Models Against Distribution Shifts
In this light, we propose a new problem of robust prompt optimization for LLMs against distribution shifts, which requires the prompt optimized over the labeled source group can simultaneously generalize to an unlabeled target group.
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM).
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs
Discrete reasoning over table-text documents (e. g., financial reports) gains increasing attention in recent two years.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples.
Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies with the prediction errors of similar training data.
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
However, such a manner inevitably learns unstable feature interactions, i. e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving.
Diffusion Recommender Model
In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner.
Generative Recommendation: Towards Next-generation Recommender Paradigm
However, such a retrieval-based recommender paradigm faces two limitations: 1) the human-generated items in the corpus might fail to satisfy the users' diverse information needs, and 2) users usually adjust the recommendations via inefficient passive feedback, e. g., clicks.
Causal Disentangled Recommendation Against User Preference Shifts
Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: 1) capturing the preference shifts across environments for accurate preference prediction, and 2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference.
On the Theories Behind Hard Negative Sampling for Recommendation
Secondly, we prove that OPAUC has a stronger connection with Top-K evaluation metrics than AUC and verify it with simulation experiments.
Mitigating Spurious Correlations for Self-supervised Recommendation
This objective requires to 1) automatically mask spurious features without supervision, and 2) block the negative effect transmission from spurious features to other features during SSL.
Unbiased Knowledge Distillation for Recommendation
Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model.
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
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.
Causal Intervention for Fairness in Multi-behavior Recommendation
Specifically, we find that: 1) item popularity is a confounder between the exposed items and users' post-click interactions, leading to the first unfairness; and 2) some hidden confounders (e. g., the reputation of item producers) affect both item popularity and quality, resulting in the second unfairness.
Causal Inference in Recommender Systems: A Survey and Future Directions
Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction.
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial recommendations (e. g., popular items) or even weird ones that are far beyond users' interests.
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Specifically, Re4 encapsulates three backward flows, i. e., 1) Re-contrast, which drives each interest embedding to be distinct from other interests using contrastive learning; 2) Re-attend, which ensures the interest-item correlation estimation in the forward flow to be consistent with the criterion used in final recommendation; and 3) Re-construct, which ensures that each interest embedding can semantically reflect the information of representative items that relate to the corresponding interest.
Towards Complex Document Understanding By Discrete Reasoning
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural Language Processing and Computer Vision.
Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network
On the one hand, their performance will dramatically degrade along with the increasing sparsity of KGs.
Structured and Natural Responses Co-generation for Conversational Search
Existing approaches either 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses directly in an end-to-end manner.
GL-RG: Global-Local Representation Granularity for Video Captioning
Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description.
Mitigating Hidden Confounding Effects for Causal Recommendation
Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback.
Addressing Confounding Feature Issue for Causal Recommendation
We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost.
Copy Motion From One to Another: Fake Motion Video Generation
3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands.
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.
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification
Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification.
WebFormer: The Web-page Transformer for Structure Information Extraction
Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price.
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.
Training Free Graph Neural Networks for Graph Matching
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free).
Learning Robust Recommender from Noisy Implicit Feedback
Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes the noisy interactions by two paradigms (i. e., Truncated Loss and Reweighted Loss).
Decoupling Strategy and Surface Realization for Task-oriented Dialogues
The core is to construct a latent content space for strategy optimization and disentangle the surface style from it.
Causal Incremental Graph Convolution for Recommender System Retraining
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.
Counterfactual Inference for Text Classification Debiasing
In inference, given a factual input document, Corsair imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model.
Empowering Language Understanding with Counterfactual Reasoning
Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning.
Deconfounded Video Moment Retrieval with Causal Intervention
To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction.
Deconfounded Recommendation for Alleviating Bias Amplification
In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounder effect of imbalanced item distribution on user representation and prediction score.
Learning Robust Recommenders through Cross-Model Agreement
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.
Ranked #1 on
Question Answering
on TAT-QA
Causal Intervention for Leveraging Popularity Bias in Recommendation
This work studies an unexplored problem in recommendation -- how to leverage popularity bias to improve the recommendation accuracy.
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.
DyHCN: Dynamic Hypergraph Convolutional Networks
In general, DyHCN consists of a Hypergraph Convolution (HC) to encode the hypergraph structure at a time point and a Temporal Evolution module (TE) to capture the varying of the relations.
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning.
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction.
Self-supervised Graph Learning for Recommendation
In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.
Bias and Debias in Recommender System: A Survey and Future Directions
This motivates us to provide a systematic survey of existing work on RS biases.
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item.
CatGCN: Graph Convolutional Networks with Categorical Node Features
In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning when the node features are categorical.
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings.
Denoising Implicit Feedback for Recommendation
In this work, we explore the central theme of denoising implicit feedback for recommender training.
How to Retrain Recommender System? A Sequential Meta-Learning Method
Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.
Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions
Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data.
Bilinear Graph Neural Network with Neighbor Interactions
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
Neural Graph Collaborative Filtering
Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
Ranked #6 on
Link Prediction
on MovieLens 25M
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization.
Ranked #3 on
Node Classification
on NELL
Explicit Interaction Model towards Text Classification
To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task.
Ranked #4 on
Text Classification
on Yahoo! Answers
Enhancing Stock Movement Prediction with Adversarial Training
The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model.
Temporal Relational Ranking for Stock Prediction
Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
Learning to Recommend with Multiple Cascading Behaviors
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
Discrete Factorization Machines for Fast Feature-based Recommendation
In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.
Neural Compatibility Modeling with Attentive Knowledge Distillation
Nevertheless, existing studies overlook the rich valuable knowledge (rules) accumulated in fashion domain, especially the rules regarding clothing matching.
