Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Subspace-Informed Matrix Completion
In this work, we consider the matrix completion problem, where the objective is to reconstruct a low-rank matrix from a few observed entries. A commonly employed approach involves nuclear norm minimization. For this meth…
Collaborative FilteringMatrix CompletionSVD-AE: Simple Autoencoders for Collaborative Filtering
Collaborative filtering (CF) methods for recommendation systems have been extensively researched, ranging from matrix factorization and autoencoder-based to graph filtering-based methods. Recently, lightweight methods th…
Collaborative FilteringFormRecommendation SystemsLEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual des…
Collaborative FilteringLanguage ModelingLanguage ModellingLarge Language Model+2Multi-Margin Cosine Loss: Proposal and Application in Recommender Systems
Recommender systems guide users through vast amounts of information by suggesting items based on their predicted preferences. Collaborative filtering-based deep learning techniques have regained popularity due to their s…
Collaborative FilteringContrastive LearningData AugmentationRecommendation SystemsA Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System
The rapid adoption of large language models (LLMs) in recommender systems (RS) presents new challenges in understanding and evaluating their biases, which can result in unfairness or the amplification of stereotypes. Tra…
BenchmarkingCollaborative FilteringcounterfactualFairness+5SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation
Graph-based collaborative filtering (CF) has emerged as a promising approach in recommender systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this pap…
Collaborative FilteringDiversityRecommendation SystemsEfficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations
Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples. This makes it hard for data-driven RSs to cater to a diverse set of users due to the…
Collaborative FilteringIn-Context LearningLearning-To-RankRecommendation SystemsDebiased Collaborative Filtering with Kernel-Based Causal Balancing
Debiased collaborative filtering aims to learn an unbiased prediction model by removing different biases in observational datasets. To solve this problem, one of the simple and effective methods is based on the propensit…
Collaborative FilteringOnline Policy Learning and Inference by Matrix Completion
Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent fea…
Collaborative FilteringDecision MakingMatrix Completionparameter estimationStructured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems
We develop a conformal inference method to construct a joint confidence region for a given group of missing entries within a sparsely observed matrix, focusing primarily on entries from the same column. Our method is mod…
Collaborative FilteringDecision MakingMatrix CompletionRecommendation SystemsRE-RecSys: An End-to-End system for recommending properties in Real-Estate domain
We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-st…
Collaborative FilteringAdvancing Recommender Systems by mitigating Shilling attacks
Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categ…
Collaborative FilteringRecommendation SystemsMixed Supervised Graph Contrastive Learning for Recommendation
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from high-order collaborative filtering signal…
Collaborative FilteringContrastive LearningMulti-Task LearningRecommendation SystemsRevealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in ea…
ClusteringCollaborative FilteringTurbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a training process. However, conventional…
Collaborative FilteringGPURecommendation SystemsCollaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. However, existing diffusion model-based r…
Collaborative FilteringComputational EfficiencyDenoisingRecommendation SystemsBeyond Collaborative Filtering: A Relook at Task Formulation in Recommender Systems
Recommender Systems (RecSys) have become indispensable in numerous applications, profoundly influencing our everyday experiences. Despite their practical significance, academic research in RecSys often abstracts the form…
Collaborative FilteringDecision MakingMissing ValuesRecommendation SystemsMARec: Metadata Alignment for cold-start Recommendation
For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks…
Collaborative FilteringRecommendation SystemsLarge Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
Collaborative filtering recommender systems (CF-RecSys) have shown successive results in enhancing the user experience on social media and e-commerce platforms. However, as CF-RecSys struggles under cold scenarios with s…
AllCollaborative FilteringRecommendation SystemsCluster-based Graph Collaborative Filtering
Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signa…
ClusteringCollaborative FilteringNode ClusteringRecommendation Systems+1