Search Results for author:
Found 31 papers, 22 papers with code
RePO: ReLU-based Preference Optimization
Aligning large language models (LLMs) with human preferences is critical for real-world deployment, yet existing methods like RLHF face computational and stability challenges.
Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions
In the realm of online digital advertising, conversion rate (CVR) prediction plays a pivotal role in maximizing revenue under cost-per-conversion (CPA) models, where advertisers are charged only when users complete specific actions, such as making a purchase.
Position-aware Graph Transformer for Recommendation
Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data.
RosePO: Aligning LLM-based Recommenders with Human Values
Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for recommendation systems, which usually adapt a pre-trained LLM to the recommendation scenario through supervised fine-tuning (SFT).
$α$-DPO: Adaptive Reward Margin is What Direct Preference Optimization Needs
Aligning large language models (LLMs) with human values and intentions is crucial for their utility, honesty, and safety.
Text-guided Diffusion Model for 3D Molecule Generation
The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery.
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences.
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
In this work, we propose a novel framework, called Invariant Graph Learning based on Information bottleneck theory (InfoIGL), to extract the invariant features of graphs and enhance models' generalization ability to unseen distributions.
Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number
ASRC obtains the final clustering results by applying RCC to the learned feature representations with their consistent graph structure and edge weights.
Reinforced Prompt Personalization for Recommendation with Large Language Models
In this work, we introduce the concept of instance-wise prompting, aiming at personalizing discrete prompts for individual users.
$β$-DPO: Direct Preference Optimization with Dynamic $β$
Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences.
Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization
We categorize noise into pointwise noise, which includes low-quality data points, and pairwise noise, which encompasses erroneous data pair associations that affect preference rankings.
Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning
Through the integration of LLMs, ToolRec enables conventional recommender systems to become external tools with a natural language interface.
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.
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment.
BSL: Understanding and Improving Softmax Loss for Recommendation
Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendation research.
LLaRA: Large Language-Recommendation Assistant
Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space.
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.
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence.
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders. Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender.
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning
In this paper, we conduct an investigation for such data augmentation in math reasoning and are intended to answer: (1) What strategies of data augmentation are more effective; (2) What is the scaling relationship between the amount of augmented data and model performance; and (3) Can data augmentation incentivize generalization to out-of-domain mathematical reasoning tasks?
Ranked #59 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Recommendation Unlearning via Influence Function
In the light that recent recommender models use historical data for both the constructions of the optimization loss and the computational graph (e. g., neighborhood aggregation), IFRU jointly estimates the direct influence of unusable data on optimization loss and the spillover influence on the computational graph to pursue complete unlearning.
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i. e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential.
GIF: A General Graph Unlearning Strategy via Influence Function
Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $\epsilon$-mass perturbation in deleted data.
Adap-$τ$: Adaptively Modulating Embedding Magnitude for Recommendation
Recent years have witnessed the great successes of embedding-based methods in recommender systems.
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.
Cross Pairwise Ranking for Unbiased Item Recommendation
In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.
On the Effectiveness of Sampled Softmax Loss for Item Recommendation
In this work, we aim to offer a better understanding of SSM for item recommendation.
Causal Attention for Interpretable and Generalizable Graph Classification
To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.
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.
Ranked #6 on
Collaborative Filtering
on Yelp2018
Graph Convolution Machine for Context-aware Recommender System
The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.
