Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering
We present LLM-KT, a flexible framework designed to enhance collaborative filtering (CF) models by seamlessly integrating LLM (Large Language Model)-generated features. Unlike existing methods that rely on passing LLM-ge…
Collaborative FilteringLanguage ModelingLanguage ModellingLarge Language Model+1Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation
Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robus…
Collaborative FilteringRecommendation SystemsSimultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training
In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences. In this setting, the models are capable of mapping users' …
AttributeCollaborative FilteringFairnessCollaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs
Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions.…
Collaborative FilteringExplainable RecommendationRecommendation SystemsTransfer LearningDomain Specific Data Distillation and Multi-modal Embedding Generation
The challenge of creating domain-centric embeddings arises from the abundance of unstructured data and the scarcity of domain-specific structured data. Conventional embedding techniques often rely on either modality, lim…
AttributeCloud ComputingCollaborative FilteringPredictionTEARS: Textual Representations for Scrutable Recommendations
Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretability. Moreover, these systems offer limite…
Collaborative FilteringRecommendation SystemsTesting Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets
The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a represe…
Collaborative FilteringDeep LearningFairnessRecommendation SystemsComprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems
Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. E…
Collaborative FilteringDiversityRecommendation SystemsNeural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities
Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just ret…
Collaborative FilteringRecommendation SystemsBridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?
Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches. Instead, gene…
Collaborative FilteringInformation RetrievalRe-RankingResponse Generation+1Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree
When annotators disagree, predicting the labels given by individual annotators can capture nuances overlooked by traditional label aggregation. We introduce three approaches to predicting individual annotator ratings on …
Collaborative FilteringIn-Context LearningPredictionSurveyUnderstanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
Collaborative Filtering (CF) methods dominate real-world recommender systems given their ability to learn high-quality, sparse ID-embedding tables that effectively capture user preferences. These tables scale linearly wi…
Collaborative FilteringRecommendation SystemsCollaborative filtering based on nonnegative/binary matrix factorization
Collaborative filtering generates recommendations based on user-item similarities through rating data, which may involve numerous unrated items. To predict scores for unrated items, matrix factorization techniques, such …
Collaborative FilteringA Hybrid Filtering for Micro-video Hashtag Recommendation using Graph-based Deep Neural Network
Due to the growing volume of user generated content, hashtags are employed as topic indicators to manage content efficiently on social media platforms. However, finding these vital topics is challenging in microvideos si…
Collaborative FilteringRecommendation SystemsGeometric Collaborative Filtering with Convergence
Latent variable collaborative filtering methods have been a standard approach to modelling user-click interactions due to their simplicity and effectiveness. However, there is limited work on analyzing the mathematical p…
Collaborative FilteringExplaining the (Not So) Obvious: Simple and Fast Explanation of STAN, a Next Point of Interest Recommendation System
A lot of effort in recent years have been expended to explain machine learning systems. However, some machine learning methods are inherently explainable, and thus are not completely black box. This enables the developer…
Collaborative FilteringPhilosophyECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System
Recommender systems are designed to suggest items based on user preferences, helping users navigate the vast amount of information available on the internet. Given the overwhelming content, outlier detection has emerged …
ClusteringCollaborative FilteringNavigateOutlier Detection+1LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
Sequential Recommender Systems (SRS), which model a user's interaction history to predict the next item of interest, are widely used in various applications. However, existing SRS often struggle with low-popularity items…
AttributeCollaborative FilteringData AugmentationLanguage Modeling+4Utilizing Collaborative Filtering in a Personalized Research-Paper Recommendation System
Recommendation system is such a platform that helps people to easily find out the things they need within a few seconds. It is implemented based on the preferences of similar users or items. In this digital era, the inte…
Collaborative FilteringRecommendation SystemsAn Efficient Multi-threaded Collaborative Filtering Approach in Recommendation System
Recommender systems are a subset of information filtering systems designed to predict and suggest items that users may find interesting or relevant based on their preferences, behaviors, or interactions. By analyzing use…
Collaborative FilteringRecommendation Systems