Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Efficient Cold-Start Recommendation via BPE Token-Level Embedding Initialization with LLM
The cold-start issue is the challenge when we talk about recommender systems, especially in the case when we do not have the past interaction data of new users or new items. Content-based features or hybrid solutions are…
Collaborative FilteringPreconditioned subgradient method for composite optimization: overparameterization and fast convergence
Composite optimization problems involve minimizing the composition of a smooth map with a convex function. Such objectives arise in numerous data science and signal processing applications, including phase retrieval, bli…
Collaborative FilteringWeightless Neural Networks for Continuously Trainable Personalized Recommendation Systems
Given that conventional recommenders, while deeply effective, rely on large distributed systems pre-trained on aggregate user data, incorporating new data necessitates large training cycles, making them slow to adapt to …
Collaborative FilteringRecommendation SystemsModel-agnostic post-hoc explainability for recommender systems
Recommender systems often benefit from complex feature embeddings and deep learning algorithms, which deliver sophisticated recommendations that enhance user experience, engagement, and revenue. However, these methods fr…
Collaborative FilteringMEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models
Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their applicability in dynamic environments whe…
Collaborative FilteringIncremental LearningBeyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation
Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across domains to enhance recommendation quality. However, naive aggregation of sequential signals can introduce conflicting domain-specific preferen…
Sequential RecommendationCollaborative FilteringELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations
Collaborative filtering drives many successful recommender systems but struggles with fine-grained user-item interactions and explainability. As users increasingly seek transparent recommendations, generating textual exp…
Collaborative FilteringText GenerationM-$LLM^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collabor…
Collaborative FilteringRecommendation SystemsSemantic SimilarityPrivacy Preserving Inference of Personalized Content for Out of Matrix Users
Recommender systems for niche and dynamic communities face persistent challenges from data sparsity, cold start users and items, and privacy constraints. Traditional collaborative filtering and content-based approaches u…
Collaborative FilteringCollaborative Filtering using Variational Quantum Hopfield Associative Memory
Quantum computing, with its ability to do exponentially faster computation compared to classical systems, has found novel applications in various fields such as machine learning and recommendation systems. Quantum Machin…
Quantum Machine LearningCollaborative FilteringRecommendation SystemsSocRipple: A Two-Stage Framework for Cold-Start Video Recommendations
Most industry scale recommender systems face critical cold start challenges new items lack interaction history, making it difficult to distribute them in a personalized manner. Standard collaborative filtering models und…
Collaborative FilteringEnd-to-End Personalization: Unifying Recommender Systems with Large Language Models
Recommender systems are essential for guiding users through the vast and diverse landscape of digital content by delivering personalized and relevant suggestions. However, improving both personalization and interpretabil…
Collaborative FilteringReasonCache: Accelerating Large Reasoning Model Serving through KV Cache Sharing
Large Reasoning Models (LRMs) are becoming integral to many AI inference systems, enhancing their capabilities with advanced reasoning. However, deploying these models in production environments presents a significant Qo…
Collaborative FilteringAI Guided Accelerator For Search Experience
Effective query reformulation is pivotal in narrowing the gap between a user's exploratory search behavior and the identification of relevant products in e-commerce environments. While traditional approaches predominantl…
Collaborative FilteringMeta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs
Generative, explainable, and flexible recommender systems, derived using Large Language Models (LLM) are promising and poorly adapted to the cold-start user situation, where there is little to no history of interaction. …
Vulnerability DetectionCollaborative FilteringDUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation
The modern recommender systems are facing an increasing challenge of modelling and predicting the dynamic and context-rich user preferences. Traditional collaborative filtering and content-based methods often struggle to…
Collaborative FilteringMovie RecommendationSGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks to harness high-order collaborative filt…
Collaborative FilteringContrastive LearningGraph LearningMulti-Task Learning+1Radial Neighborhood Smoothing Recommender System
Recommender systems inherently exhibit a low-rank structure in latent space. A key challenge is to define meaningful and measurable distances in the latent space to capture user-user, item-item, user-item relationships e…
Collaborative FilteringNLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Graph Neural Networks (GNNs) are widely used in collaborative filtering to capture high-order user-item relationships. To address the data sparsity problem in recommendation systems, Graph Contrastive Learning (GCL) has …
Collaborative FilteringContrastive LearningRecommendation SystemsFrom ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
Most existing multimodal collaborative filtering recommendation (MCFRec) methods rely heavily on ID features and multimodal content to enhance recommendation performance. However, this paper reveals that ID features are …
Collaborative FilteringContrastive Learning