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Found 46 papers, 15 papers with code
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Every day the volume of training data is expanding and the number of user interactions is constantly increasing.
Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization
Furthermore, we illustrate the underlying aspects of the proposed model in effectively capturing extensive temporal dependencies in dynamic graphs.
Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems
Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive interactions in industrial scenarios.
TEAL: Tokenize and Embed ALL for Multi-modal Large Language Models
Specifically, for the input from any modality, TEAL first discretizes it into a token sequence with the off-the-shelf tokenizer and embeds the token sequence into a joint embedding space with a learnable embedding matrix.
Multi-resolution Time-Series Transformer for Long-term Forecasting
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
Towards Automated Negative Sampling in Implicit Recommendation
Hence, we propose a hypothesis that the negative sampler should align with the capacity of the recommendation models as well as the statistics of the datasets to achieve optimal performance.
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.
Dynamically Expandable Graph Convolution for Streaming Recommendation
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.
A Survey on User Behavior Modeling in Recommender Systems
Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.
Spectral Augmentations for Graph Contrastive Learning
Contrastive learning has emerged as a premier method for learning representations with or without supervision.
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
This performance relies heavily on the configuration of the network parameters.
Findings of the WMT 2022 Shared Task on Translation Suggestion
We report the result of the first edition of the WMT shared task on Translation Suggestion (TS).
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation
One of the challenges in studying the interactions in large graphs is to learn their diverse pattern and various interaction types.
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.
Ranked #1 on
Dynamic Link Prediction
on Social Evolution
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models.
Ranked #2 on
Click-Through Rate Prediction
on KDD12
Node Copying: A Random Graph Model for Effective Graph Sampling
In this work, we introduce the node copying model for constructing a distribution over graphs.
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
Learning vectorized embeddings is at the core of various recommender systems for user-item matching.
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks
Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks.
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.
Content Filtering Enriched GNN Framework for News Recommendation
It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously.
WeTS: A Benchmark for Translation Suggestion
To break this limitation, we create a benchmark data set for TS, called \emph{WeTS}, which contains golden corpus annotated by expert translators on four translation directions.
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention.
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
Dual Graph enhanced Embedding Neural Network for CTR Prediction
To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition
Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse.
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.
On Batch-size Selection for Stochastic Training for Graph Neural Networks
In recent years deep learning has become an important framework for supervised learning.
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection
As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the question and the candidate.
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion.
A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels.
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial.
Multi-Graph Convolution Collaborative Filtering
In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.
Memory Augmented Graph Neural Networks for Sequential Recommendation
In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations.
A Graph-CNN for 3D Point Cloud Classification
Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph.
Bayesian graph convolutional neural networks for semi-supervised classification
Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion.
Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence
To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel.
