Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Neural Graph Collaborative Filtering Using Variational Inference
The customization of recommended content to users holds significant importance in enhancing user experiences across a wide spectrum of applications such as e-commerce, music, and shopping. Graph-based methods have achiev…
Collaborative FilteringRecommendation SystemsVariational InferenceCollaborative Word-based Pre-trained Item Representation for Transferable Recommendation
Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually utilize ID embeddings to represent items, …
Collaborative FilteringRecommendation SystemsRepresentation LearningSequential RecommendationKnowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
The significant progress of large language models (LLMs) provides a promising opportunity to build human-like systems for various practical applications. However, when applied to specific task domains, an LLM pre-trained…
Collaborative FilteringRecommendation SystemsTask 2Graph Signal Diffusion Model for Collaborative Filtering
Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly developed diffusion model shows great potential…
Collaborative FilteringDenoisingmodelRecommendation SystemsUser Persona Identification and New Service Adaptation Recommendation
Providing a personalized user experience on information dense webpages helps users in reaching their end-goals sooner. We explore an automated approach to identifying user personas by leveraging high dimensional trajecto…
Collaborative FilteringLanguage ModelingLanguage ModellingMasked Language ModelingAlleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most …
Collaborative FilteringMulti-Task LearningRepresentation LearningOn the steerability of large language models toward data-driven personas
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented. Here, we present a novel approach to achieve controllable generation of speci…
Collaborative FilteringLanguage ModellingLarge Language ModelImproving Collaborative Filtering Recommendation via Graph Learning
Recommendation systems are designed to provide personalized predictions for items that are most appealing to individual customers. Among various types of recommendation algorithms, k-nearest neighbor based collaborative …
Collaborative FilteringGraph LearningRecommendation SystemsHeterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces
Recommendation systems (RS) for items (e.g., movies, books) and ads are widely used to tailor content to users on various internet platforms. Traditionally, recommendation models are trained on a central server. However,…
Collaborative FilteringFederated LearningRecommendation SystemsA Collaborative Filtering-Based Two Stage Model with Item Dependency for Course Recommendation
Recommender systems have been studied for decades with numerous promising models been proposed. Among them, Collaborative Filtering (CF) models are arguably the most successful one due to its high accuracy in recommendat…
Collaborative FilteringRecommendation SystemsLFG: A Generative Network for Real-Time Recommendation
Recommender systems are essential information technologies today, and recommendation algorithms combined with deep learning have become a research hotspot in this field. The recommendation model known as LFM (Latent Fact…
Collaborative FilteringMovie RecommendationRecommendation SystemsBlocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints
We consider the problem of \emph{blocked} collaborative bandits where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean r…
Collaborative FilteringMatrix CompletionLightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation
Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems. Most recent reports, however, focus heavily on new model architectures. This may bring some gaps when applying GNN in the…
Collaborative Filteringgraph constructionGraph Neural NetworkRetrievalEmbedding in Recommender Systems: A Survey
Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that coverts the high-dimensional discrete featur…
AutoMLCollaborative FilteringQuantizationRecommendation Systems+2Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into collaborative filtering (CF) is prevaili…
Collaborative FilteringContrastive LearningData AugmentationRecommendation Systems+1Topology-aware Debiased Self-supervised Graph Learning for Recommendation
In recommendation, graph-based Collaborative Filtering (CF) methods mitigate the data sparsity by introducing Graph Contrastive Learning (GCL). However, the random negative sampling strategy in these GCL-based CF models …
Collaborative FilteringContrastive LearningGraph LearningSemantic Similarity+1Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering
Current item-item collaborative filtering algorithms based on artificial neural network, such as Item2vec, have become ubiquitous and are widely applied in the modern recommender system. However, these approaches do not …
Collaborative FilteringGraph EmbeddingRecommendation SystemsBatch-Mix Negative Sampling for Learning Recommendation Retrievers
Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real s…
Collaborative FilteringSelection biasKG4Ex: An Explainable Knowledge Graph-Based Approach for Exercise Recommendation
Effective exercise recommendation is crucial for guiding students' learning trajectories and fostering their interest in the subject matter. However, the vast exercise resource and the varying learning abilities of indiv…
Collaborative FilteringOn Generative Agents in Recommendation
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development. Addressing this challenge, we envision a re…
Collaborative FilteringMovie RecommendationRecommendation Systems