Search Results for author:
Found 46 papers, 22 papers with code
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations.
Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
We consider the conditional generation of 3D drug-like molecules with \textit{explicit control} over molecular properties such as drug-like properties (e. g., Quantitative Estimate of Druglikeness or Synthetic Accessibility score) and effectively binding to specific protein sites.
A Survey on LLM Inference-Time Self-Improvement
Techniques that enhance inference through increased computation at test-time have recently gained attention.
ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning
This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations.
Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
To address this dilemma, we introduce TaylorMLP to protect the ownership of released LLMs and prevent their abuse.
TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
Our theoretical analysis and experimental results show that the proposed model optimizing alignment and uniformity with the twin encoder contributes to better recommendation accuracy and training efficiency performance.
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking
API call generation is the cornerstone of large language models' tool-using ability that provides access to the larger world.
Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation
Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions.
Countering Mainstream Bias via End-to-End Adaptive Local Learning
In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance.
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) along with ranking and re-ranking techniques, to improve the factuality of long-form question answering (QA) in the medical domain.
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors.
Large Language Models as Data Augmenters for Cold-Start Item Recommendation
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems.
Disclosure and Mitigation of Gender Bias in LLMs
Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs.
The Neglected Tails in Vision-Language Models
We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts.
$DA^3$: A Distribution-Aware Adversarial Attack against Language Models
We further design a novel evaluation metric, the Non-detectable Attack Success Rate (NASR), which integrates both ASR and detectability for the attack task.
Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation
Large Language Models (LLMs) can generate biased and toxic responses.
Co$^2$PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Pre-trained Language Models are widely used in many important real-world applications.
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model
Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems.
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services.
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs.
Automated Data Denoising for Recommendation
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback.
Enhancing User Personalization in Conversational Recommenders
Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience.
Evolution of Filter Bubbles and Polarization in News Recommendation
However, most existing work focuses on a static setting or over a short-time window, leaving open questions about the long-term and dynamic impacts of news recommendations.
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.
Closed-book Question Generation via Contrastive Learning
Through experiments, we validate the proposed QG model on both public datasets and a new WikiCQA dataset.
Ranked #2 on
Open-Domain Question Answering
on ELI5
Towards Fair Conversational Recommender Systems
Conversational recommender systems have demonstrated great success.
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while effectively guiding users to a more personalized recommendation.
Evolution of Popularity Bias: Empirical Study and Debiasing
and how to debias in this long-term dynamic process?
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
Specifically, in each training iteration and adaptively for each part of the network, the gradient of an auxiliary loss is carefully reduced or enlarged to have a closer magnitude to the gradient of the target loss, preventing auxiliary tasks from being so strong that dominate the target task or too weak to help the target task.
Session-based Recommendation with Hypergraph Attention Networks
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks.
Sequential Recommendation for Cold-start Users with Meta Transitional Learning
A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items.
Identifying Hijacked Reviews
Fake reviews and review manipulation are growing problems on online marketplaces globally.
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features.
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
This is paper is an extended and reorganized version of our SIGIR 2020~\cite{zhu2020measuring} paper.
Popularity-Opportunity Bias in Collaborative Filtering
This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias.
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition
Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment.
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge
We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
Understanding Car-Speak: Replacing Humans in Dealerships
A large portion of the car-buying experience in the United States involves interactions at a car dealership.
Consistency-Aware Recommendation for User-Generated ItemList Continuation
Complementary to methods that exploit specific content patterns (e. g., as in song-based playlists that rely on audio features), the proposed approach models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types (e. g., videos, images, books).
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists
User-generated item lists are a popular feature of many different platforms.
Fairness-Aware Recommendation of Information Curators
This paper highlights our ongoing efforts to create effective information curator recommendation models that can be personalized for individual users, while maintaining important fairness properties.
Tensor Completion Algorithms in Big Data Analytics
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors.
Online Deception Detection Refueled by Real World Data Collection
The lack of large realistic datasets presents a bottleneck in online deception detection studies.
