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Found 30 papers, 15 papers with code
Federated Conversational Recommender System
To address the user privacy concerns in CRS, we first define a set of privacy protection guidelines for preserving user privacy under the conversational recommendation setting.
Flow Matching for Collaborative Filtering
Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences.
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.
SGSST: Scaling Gaussian Splatting StyleTransfer
Applying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering.
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.
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.
LLMs for User Interest Exploration in Large-scale Recommendation Systems
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel user interests.
Empowering Large Language Models for Textual Data Augmentation
With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation.
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.
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.
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.
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
The multi-funnel setup effectively balances between coverage and relevance.
Learning Strong Graph Neural Networks with Weak Information
Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
Enhancing User Personalization in Conversational Recommenders
Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience.
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.
Learning to Augment for Casual User Recommendation
Users who come to recommendation platforms are heterogeneous in activity levels.
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.
Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model
Online video services acquire new content on a daily basis to increase engagement, and improve the user experience.
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.
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.
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.
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Text classification is a critical research topic with broad applications in natural language processing.
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.
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.
