Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
When Should we Expect Non-Decreasing Returns from Data in Prediction Tasks?
This article studies the change in the prediction accuracy of a response variable when the number of predictors increases, and all variables follow a multivariate normal distribution. Assuming that the correlations betwe…
Collaborative FilteringCoPL: Collaborative Preference Learning for Personalizing LLMs
Personalizing large language models (LLMs) is important for aligning outputs with diverse user preferences, yet existing methods struggle with flexibility and generalization. We propose CoPL (Collaborative Preference Lea…
Collaborative FilteringLLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Collaborative filtering models, particularly graph-based approaches, have demonstrated strong performance in capturing user-item interactions for recommendation systems. However, they continue to struggle in cold-start a…
Collaborative FilteringRecommendation SystemsHI-Series Algorithms A Hybrid of Substance Diffusion Algorithm and Collaborative Filtering
Recommendation systems face the challenge of balancing accuracy and diversity, as traditional collaborative filtering (CF) and network-based diffusion algorithms exhibit complementary limitations. While item-based CF (It…
Collaborative FilteringDiversityRecommendation SystemsEnhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis
Massive Open Online Courses (MOOCs) are emerging as a popular alternative to traditional education, offering learners the flexibility to access a wide range of courses from various disciplines, anytime and anywhere. Desp…
Collaborative FilteringRecommendation SystemsSurvival AnalysisTime-to-Event PredictionTraining Large Recommendation Models via Graph-Language Token Alignment
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, traditional RS relying on Collaborative Filt…
Collaborative FilteringNavigateRecommendation SystemsJoint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation
Cross-Domain Recommendation (CDR) has been widely investigated for solving long-standing data sparsity problem via knowledge sharing across domains. In this paper, we focus on the Multi-Modal Cross-Domain Recommendation …
Collaborative FilteringDomain AdaptationTransfer LearningSuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disci…
Collaborative FilteringUnbiased Collaborative Filtering with Fair Sampling
Recommender systems leverage extensive user interaction data to model preferences; however, directly modeling these data may introduce biases that disproportionately favor popular items. In this paper, we demonstrate tha…
Collaborative FilteringFairnessRecommendation SystemsCollaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their superior understanding of context-aware…
Collaborative FilteringConversational RecommendationLanguage ModelingLanguage Modelling+4G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness, and trustworthiness. To provide persona…
Collaborative FilteringExplainable RecommendationLanguage ModelingLanguage Modelling+2Order-agnostic Identifier for Large Language Model-based Generative Recommendation
Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning item identifiers for LLMs to encode use…
Collaborative FilteringLanguage ModelingLanguage ModellingLarge Language ModelCriteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation
Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborativ…
Collaborative FilteringComputational EfficiencyRecommendation SystemsUse of Air Quality Sensor Network Data for Real-time Pollution-Aware POI Suggestion
This demo paper introduces AirSense-R, a privacy-preserving mobile application that delivers real-time, pollution-aware recommendations for urban points of interest (POIs). By merging live air quality data from AirSENCE …
Collaborative FilteringDecision MakingFederated LearningPrivacy PreservingCan this Model Also Recognize Dogs? Zero-Shot Model Search from Weights
With the increasing numbers of publicly available models, there are probably pretrained, online models for most tasks users require. However, current model search methods are rudimentary, essentially a text-based search …
Collaborative FilteringmodelRetrievalCollaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information
Graph Neural Networks (GNNs) have demonstrated their superiority in collaborative filtering, where the user-item (U-I) interaction bipartite graph serves as the fundamental data format. However, when graph-structured sid…
Collaborative FilteringMultimodal RecommendationFlow Matching for Collaborative Filtering
Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences. However, existing approaches struggle with inaccurate posterior approximat…
Collaborative FilteringRecommendation SystemsMIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling
Click-Through Rate (CTR) prediction is a crucial task in recommendation systems, online searches, and advertising platforms, where accurately capturing users' real interests in content is essential for performance. Howev…
Click-Through Rate PredictionCollaborative FilteringRecommendation SystemsHypergraph Diffusion for High-Order Recommender Systems
Recommender systems rely on Collaborative Filtering (CF) to predict user preferences by leveraging patterns in historical user-item interactions. While traditional CF methods primarily focus on learning compact vector em…
Collaborative FilteringContrastive LearningGraph Neural NetworkRecommendation SystemsGraph Neural Controlled Differential Equations For Collaborative Filtering
Graph Convolution Networks (GCNs) are widely considered state-of-the-art for recommendation systems. Several studies in the field of recommendation systems have attempted to apply collaborative filtering (CF) into the Ne…
Collaborative FilteringRecommendation Systems