Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories
Human trajectory anomaly detection has become increasingly important across a wide range of applications, including security surveillance and public health. However, existing trajectory anomaly detection methods are prim…
Anomaly DetectionCollaborative FilteringTwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
In the domain of recommendation and collaborative filtering, Graph Contrastive Learning (GCL) has become an influential approach. Nevertheless, the reasons for the effectiveness of contrastive learning are still not well…
Collaborative FilteringContrastive LearningData AugmentationA Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios
Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions. Therefore, the performance of CF algorithms degrades when few or no interactions are availabl…
Collaborative FilteringMultimodal RecommendationRecommendation SystemsA Prompting-Based Representation Learning Method for Recommendation with Large Language Models
In recent years, Recommender Systems (RS) have witnessed a transformative shift with the advent of Large Language Models (LLMs) in the field of Natural Language Processing (NLP). Models such as GPT-3.5/4, Llama, have dem…
Collaborative FilteringProfile GenerationRecommendation SystemsRepresentation LearningFusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search
Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for…
Collaborative FilteringCPUGPURe-RankingEDGE-Rec: Efficient and Data-Guided Edge Diffusion For Recommender Systems Graphs
Most recommender systems research focuses on binary historical user-item interaction encodings to predict future interactions. User features, item features, and interaction strengths remain largely under-utilized in this…
Collaborative FilteringDenoisingImage GenerationRecommendation SystemsRevisiting BPR: A Replicability Study of a Common Recommender System Baseline
Bayesian Personalized Ranking (BPR), a collaborative filtering approach based on matrix factorization, frequently serves as a benchmark for recommender systems research. However, numerous studies often overlook the nuanc…
Collaborative FilteringRecommendation SystemsThe trade-off between data minimization and fairness in collaborative filtering
General Data Protection Regulations (GDPR) aim to safeguard individuals' personal information from harm. While full compliance is mandatory in the European Union and the California Privacy Rights Act (CPRA), it is not in…
Active LearningCollaborative FilteringFairnessRecommendation SystemsFLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement
Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an item ID-only model, such as Bert4Rec. I…
Collaborative FilteringLanguage ModelingLanguage ModellingLarge Language Model+1Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search
Personalized search has been extensively studied in various applications, including web search, e-commerce, social networks, etc. With the soaring popularity of short-video platforms, exemplified by TikTok and Kuaishou, …
Collaborative FilteringMulti-Task LearningRetrievalbeeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems
Recommender systems often use text-side information to improve their predictions, especially in cold-start or zero-shot recommendation scenarios, where traditional collaborative filtering approaches cannot be used. Many …
Collaborative FilteringRecommendation SystemsSemantic SimilaritySemantic Textual Similarity+1Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks
Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable insights for predictions, new music piece…
Collaborative FilteringMusic RecommendationRecommendation SystemsMultilingual Prompts in LLM-Based Recommenders: Performance Across Languages
Large language models (LLMs) are increasingly used in natural language processing tasks. Recommender systems traditionally use methods such as collaborative filtering and matrix factorization, as well as advanced techniq…
Collaborative FilteringRecommendation SystemsEnhancing Sequential Recommendations through Multi-Perspective Reflections and Iteration
Sequence recommendation (SeqRec) aims to predict the next item a user will interact with by understanding user intentions and leveraging collaborative filtering information. Large language models (LLMs) have shown great …
Collaborative FilteringGPUEnhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation
Graph neural network(GNN) has been a powerful approach in collaborative filtering(CF) due to its ability to model high-order user-item relationships. Recently, to alleviate the data sparsity and enhance representation le…
Collaborative FilteringContrastive LearningGraph Neural NetworkRepresentation LearningImputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning
This paper extends the recently developed framework of multilinear kernel regression and imputation via manifold learning (MultiL-KRIM) to impute time-varying edge flows in a graph. MultiL-KRIM uses simplicial-complex ar…
Collaborative FilteringDimensionality ReductionImputationImproved Diversity-Promoting Collaborative Metric Learning for Recommendation
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing p…
Collaborative FilteringDiversityMetric LearningRecommendation SystemsPDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data
The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It is well known that collaborative filterin…
Collaborative FilteringDiversityPrivacy PreservingA Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter
In this paper, a color edge detection strategy based on collaborative filtering combined with multiscale gradient fusion is proposed. The block-matching and 3D (BM3D) filter are used to enhance the sparse representation …
Collaborative FilteringDenoisingEdge DetectionCF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniques have evolved into sophisticated mode…
Collaborative FilteringContinual LearningKolmogorov-Arnold NetworksRecommendation Systems