Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting
We present a novel method for matrix completion, specifically designed for matrices where one dimension significantly exceeds the other. Our Columns Selected Matrix Completion (CSMC) method combines Column Subset Selecti…
Collaborative FilteringImage InpaintingLow-Rank Matrix CompletionMatrix Completion+2Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health rec…
Collaborative FilteringPatient PhenotypingTime SeriesDeep Rating Elicitation for New Users in Collaborative Filtering
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations. The key challenge of the…
Collaborative FilteringRecommendation SystemsInteraRec: Screenshot Based Recommendations Using Multimodal Large Language Models
Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests. Numerous recommendation algorithms, employing strategies such as collaborati…
Collaborative FilteringInteractive RecommendationNavigateRecommendation Systems+1Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation with Interpretability
Multimodal recommender systems amalgamate multimodal information (e.g., textual descriptions, images) into a collaborative filtering framework to provide more accurate recommendations. While the incorporation of multimod…
Collaborative FilteringMultimodal RecommendationRecommendation SystemsEstimating Unknown Population Sizes Using the Hypergeometric Distribution
The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into multiple categories. Addressing a gap in the literature, we tackle the challenge of …
Collaborative FilteringScalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has …
Collaborative FilteringExposure FairnessFairnessRecommendation SystemsGeneral Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
Graph neural networks (GNNs) have shown impressive performance in recommender systems, particularly in collaborative filtering (CF). The key lies in aggregating neighborhood information on a user-item interaction graph t…
Collaborative FilteringRecommendation SystemsRepresentation LearningNeighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to introduce additional self-supervised signals…
Collaborative FilteringContrastive LearningData AugmentationRecommendation SystemsFrom Variability to Stability: Advancing RecSys Benchmarking Practices
In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approac…
BenchmarkingCollaborative FilteringRecommendation SystemsLarge Language Model Simulator for Cold-Start Recommendation
Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely solely on content features, limiting their …
Collaborative FilteringLanguage ModelingLanguage ModellingLarge Language Model+2Frequency-aware Graph Signal Processing for Collaborative Filtering
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the importance of various interactions that re…
Collaborative FilteringHelping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization
The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help …
Collaborative FilteringRecommendation SystemsMulti-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors naturally form separate behavior graphs and l…
Collaborative FilteringRecommendation SystemsCollaborative filtering, K-nearest neighbor and cosine similarity in home decor recommender systems
An architectural framework, based on collaborative filtering using K-nearest neighbor and cosine similarity, was developed and implemented to fit the requirements for the company DecorRaid. The aim of the paper is to tes…
Collaborative FilteringRecommendation SystemsPrototypical Contrastive Learning through Alignment and Uniformity for Recommendation
Graph Collaborative Filtering (GCF), one of the most widely adopted recommendation system methods, effectively captures intricate relationships between user and item interactions. Graph Contrastive Learning (GCL) based G…
Collaborative FilteringContrastive LearningLink PredictionEnhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security
Urban region profiling plays a crucial role in forecasting and decision-making in the context of dynamic and noisy urban environments. Existing methods often struggle with issues such as noise, data incompleteness, and s…
Adversarial AttackCollaborative FilteringContrastive LearningCrime Prediction+3A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with legal, ethical, or economic implications.…
Collaborative FilteringFairnessModel SelectionRecommendation Systems+1CF4J: Collaborative Filtering for Java
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different RS features. It is advisable to use RS f…
Collaborative FilteringRecommendation SystemsRecDCL: Dual Contrastive Learning for Recommendation
Self-supervised learning (SSL) has recently achieved great success in mining the user-item interactions for collaborative filtering. As a major paradigm, contrastive learning (CL) based SSL helps address data sparsity in…
Collaborative FilteringContrastive LearningRepresentation LearningSelf-Supervised Learning