Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs
Self-Attentive Sequential Recommendation (SASRec) effectively captures long-term user preferences by applying attention mechanisms to historical interactions. Concurrently, the rise of Large Language Models (LLMs) has mo…
Sequential RecommendationCollaborative FilteringContent filtering methods for music recommendation: A review
Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is collaborative filtering, which suggests…
Collaborative FilteringRecommendation SystemsRAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
This paper addresses the challenge of developing multimodal recommender systems for the movie domain, where limited metadata (e.g., title, genre) often hinders the generation of robust recommendations. We introduce a res…
Collaborative FilteringData AugmentationMulti-modal RecommendationRAG+3LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly…
Collaborative FilteringSequential RecommendationCORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Recommender systems (RSs) are designed to retrieve candidate items a user might be interested in from a large pool. A common approach is using graph neural networks (GNNs) to capture high-order interaction relationships.…
Collaborative FilteringRecommendation SystemsRe-RankingRetrievalDual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Disentangling user intentions from implicit feedback has become a promising strategy to enhance recommendation accuracy and interpretability. Prior methods often model intentions independently and lack explicit supervisi…
Collaborative FilteringDisentanglementDiscrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
Metric learning has attracted extensive interest for its ability to provide personalized recommendations based on the importance of observed user-item interactions. Current metric learning methods aim to push negative it…
Collaborative FilteringMetric LearningRecommendation SystemsBridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Recent works of music representation learning mainly focus on learning acoustic music representations with unlabeled audios or further attempt to acquire multi-modal music representations with scarce annotated audio-text…
Collaborative FilteringContrastive LearningMusic RecommendationRepresentation LearningLight distillation for Incremental Graph Convolution Collaborative Filtering
Recommender systems presently utilize vast amounts of data and play a pivotal role in enhancing user experiences. Graph Convolution Networks (GCNs) have surfaced as highly efficient models within the realm of recommender…
Collaborative FilteringKnowledge DistillationRecommendation SystemsLightweight Embeddings with Graph Rewiring for Collaborative Filtering
As recommendation services scale rapidly and their deployment now commonly involves resource-constrained edge devices, GNN-based recommender systems face significant challenges, including high embedding storage costs and…
Collaborative FilteringRecommendation SystemsPersonalized Diffusion Model Reshapes Cold-Start Bundle Recommendation
Bundle recommendation aims to recommend a set of items to each user. However, the sparser interactions between users and bundles raise a big challenge, especially in cold-start scenarios. Traditional collaborative filter…
Collaborative FilteringPredictively Combatting Toxicity in Health-related Online Discussions through Machine Learning
In health-related topics, user toxicity in online discussions frequently becomes a source of social conflict or promotion of dangerous, unscientific behaviour; common approaches for battling it include different forms of…
Collaborative FilteringOn the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective
Collaborative filtering (CF) enables large-scale recommendation systems by encoding information from historical user-item interactions into dense ID-embedding tables. However, as embedding tables grow, closed-form soluti…
Collaborative FilteringRecommendation SystemsUser and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
Data is an essential resource for studying recommender systems. While there has been significant work on improving and evaluating state-of-the-art models and measuring various properties of recommender system outputs, le…
Collaborative FilteringDiversityFairnessRecommendation SystemsUncertainty in Repeated Implicit Feedback as a Measure of Reliability
Recommender systems rely heavily on user feedback to learn effective user and item representations. Despite their widespread adoption, limited attention has been given to the uncertainty inherent in the feedback used to …
Collaborative FilteringRecommendation SystemsSimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
Deep Neural Networks (DNNs) are extensively used in collaborative filtering due to their impressive effectiveness. These systems depend on interaction data to learn user and item embeddings that are crucial for recommend…
Collaborative FilteringData AugmentationFairnessPreserving Privacy and Utility in LLM-Based Product Recommendations
Large Language Model (LLM)-based recommendation systems leverage powerful language models to generate personalized suggestions by processing user interactions and preferences. Unlike traditional recommendation systems th…
Collaborative FilteringLarge Language ModelPrivacy PreservingRecommendation SystemsGraph Spectral Filtering with Chebyshev Interpolation for Recommendation
Graph convolutional networks have recently gained prominence in collaborative filtering (CF) for recommendations. However, we identify potential bottlenecks in two foundational components. First, the embedding layer lead…
Collaborative FilteringLearning Universal User Representations Leveraging Cross-domain User Intent at Snapchat
The development of powerful user representations is a key factor in the success of recommender systems (RecSys). Online platforms employ a range of RecSys techniques to personalize user experience across diverse in-app s…
Collaborative FilteringFormRecommendation SystemsRetrievalOptimal Sequential Recommendations: Exploiting User and Item Structure
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. A latent variable model specifies the user preferences: both …
Collaborative FilteringRecommendation Systems