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Found 22 papers, 14 papers with code
AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System
Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training.
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.
Graph-based Alignment and Uniformity for Recommendation
To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph.
MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System
To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec).
Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.
Knowledge Graph Context-Enhanced Diversified Recommendation
Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph
PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users.
Unified Pretraining for Recommendation via Task Hypergraphs
On the other hand, pretraining and finetuning on the same dataset leads to a high risk of overfitting.
Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation
We explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation.
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems.
Federated Social Recommendation with Graph Neural Network
However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns.
Conditional Denoising Diffusion for Sequential Recommendation
Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions.
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
With the proliferation of social media, a growing number of users search for and join group activities in their daily life.
Collaborative Semantic Alignment in Recommendation Systems
However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.
Multi-view Graph Convolution for Participant Recommendation
Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate.
Cyclic Neural Network
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning
In the DWHRec algorithm, the initial connections between users and listened tracks are represented by a weighted hypergraph.
Instruction-based Hypergraph Pretraining
However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrained knowledge.
