Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion
This demo paper presents \airtown, a privacy-preserving mobile application that provides real-time, pollution-aware recommendations for points of interest (POIs) in urban environments. By combining real-time Air Quality …
Collaborative FilteringFederated LearningPrivacy PreservingEnhancing User Intent for Recommendation Systems via Large Language Models
Recommendation systems play a critical role in enhancing user experience and engagement in various online platforms. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), rely heavi…
Collaborative FilteringRecommendation SystemsDiffusion Models in Recommendation Systems: A Survey
Recommender systems remain an essential topic due to its wide application in various domains and the business potential behind them. With the rise of deep learning, common solutions have leveraged neural networks to faci…
Collaborative FilteringRecommendation SystemsSurveyKGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion
While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient …
AttributeCollaborative FilteringGraph AttentionKnowledge Graph Embeddings+3The Application of Large Language Models in Recommendation Systems
The integration of Large Language Models into recommendation frameworks presents key advantages for personalization and adaptability of experiences to the users. Classic methods of recommendations, such as collaborative …
Collaborative FilteringDiversityRecommendation SystemsSpk2SRImgNet: Super-Resolve Dynamic Scene from Spike Stream via Motion Aligned Collaborative Filtering
Spike camera is a kind of neuromorphic camera that records dynamic scenes by firing a stream of binary spikes with extremely high temporal resolution. It demonstrates great potential for vision tasks in high-speed sc…
Collaborative FilteringImage RestorationSuper-ResolutionS-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain
Recovering user preferences from user-item interaction matrices is a key challenge in recommender systems. While diffusion models can sample and reconstruct preferences from latent distributions, they often fail to captu…
Collaborative FilteringDenoisingRecommendation SystemsHgformer: Hyperbolic Graph Transformer for Recommendation
The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effective method to alleviate the cold start pro…
Collaborative FilteringRecommendation SystemsRecommender Engine Driven Client Selection in Federated Brain Tumor Segmentation
This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, t…
Brain Tumor SegmentationCollaborative FilteringFederated LearningSegmentation+1RecLM: Recommendation Instruction Tuning
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Networks (GNNs) excel at capturing user-ite…
Collaborative FilteringDiversityLanguage ModellingRecommendation SystemsOptimization and Scalability of Collaborative Filtering Algorithms in Large Language Models
With the rapid development of large language models (LLMs) and the growing demand for personalized content, recommendation systems have become critical in enhancing user experience and driving engagement. Collaborative f…
Collaborative FilteringComputational EfficiencyModel CompressionRecommendation SystemsEnhanced Recommendation Combining Collaborative Filtering and Large Language Models
With the advent of the information explosion era, the importance of recommendation systems in various applications is increasingly significant. Traditional collaborative filtering algorithms are widely used due to their …
Collaborative FilteringDiversityNatural Language UnderstandingRecommendation SystemsPosition-aware Graph Transformer for Recommendation
Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data. Recently, graph convolution networks (GCNs) have advanced the field by utilizing high-order c…
Collaborative FilteringPositionMolar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation
Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more recently, to large language models (LLM…
Collaborative FilteringSequential RecommendationUnlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge. Current research focuses on addressing the challenge of…
Collaborative FilteringRecommendation SystemsFrom Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking
Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such full rankings in practice, most CF models r…
Collaborative FilteringOrdinal ClassificationMixRec: Heterogeneous Graph Collaborative Filtering
For modern recommender systems, the use of low-dimensional latent representations to embed users and items based on their observed interactions has become commonplace. However, many existing recommendation models are pri…
Collaborative FilteringContrastive LearningData AugmentationDisentanglement+1STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation
While the mining of modalities is the focus of most multimodal recommendation methods, we believe that how to fully utilize both collaborative and multimodal information is pivotal in e-commerce scenarios where, as clari…
Collaborative FilteringMultimodal RecommendationA multi-theoretical kernel-based approach to social network-based recommendation
Recommender systems are a critical component of e-commercewebsites. The rapid development of online social networking services provides an opportunity to explore social networks together with information used in traditio…
Collaborative FilteringRecommendation SystemsMovie Recommendation using Web Crawling
In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites …
Collaborative FilteringMovie Recommendation