Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Meta Decision Trees for Explainable Recommendation Systems
We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regre…
AttributeCollaborative FilteringExplainable RecommendationRecommendation Systems+1An Adaptive Similarity Measure to Tune Trust Influence in Memory-Based Collaborative Filtering
The aim of the recommender systems is to provide relevant and potentially interesting information to each user. This is fulfilled by utilizing the already recorded tendencies of similar users or detecting items similar t…
Collaborative FilteringRecommendation SystemsCollaborative Filtering vs. Content-Based Filtering: differences and similarities
Recommendation Systems (SR) suggest items exploring user preferences, helping them with the information overload problem. Two approaches to SR have received more prominence, Collaborative Filtering, and Content-Based Fil…
Collaborative FilteringRecommendation SystemsFast Computation of Katz Index for Efficient Processing of Link Prediction Queries
Network proximity computations are among the most common operations in various data mining applications, including link prediction and collaborative filtering. A common measure of network proximity is Katz index, which h…
Collaborative FilteringLink PredictionPredictionBalancing Accuracy and Diversity in Recommendations using Matrix Completion Framework
Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area. However, several studies have suggested the need for diversified recommendations, with acceptable level of accuracy, …
Collaborative FilteringDiversityMatrix CompletionRecommendation SystemsDeep Latent Factor Model for Collaborative Filtering
Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the…
BIG-bench Machine LearningCollaborative FilteringDeep Learningmodel+1Addressing Marketing Bias in Product Recommendations
Modern collaborative filtering algorithms seek to provide personalized product recommendations by uncovering patterns in consumer-product interactions. However, these interactions can be biased by how the product is mark…
Collaborative FilteringFairnessMarketingRecommendation SystemsData Poisoning Attacks on Neighborhood-based Recommender Systems
Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effec…
Collaborative FilteringData PoisoningRecommendation SystemsMatrix Completion using Kronecker Product Approximation
A matrix completion problem is to recover the missing entries in a partially observed matrix. Most of the existing matrix completion methods assume a low rank structure of the underlying complete matrix. In this paper, w…
Collaborative FilteringDenoisingMatrix CompletionMulti-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collabo…
Collaborative FilteringRecommendation SystemsOn Universal Features for High-Dimensional Learning and Inference
We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we introduce natural notions of universality and…
Collaborative FilteringregressionVocal Bursts Intensity PredictionOptimal Robust Learning of Discrete Distributions from Batches
Many applications, including natural language processing, sensor networks, collaborative filtering, and federated learning, call for estimating discrete distributions from data collected in batches, some of which may be …
Collaborative FilteringFederated LearningA Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) t…
Collaborative FilteringRecommendation SystemsCollaborative Distillation for Top-N Recommendation
Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model. Despite the success of KD in the classification task, applying KD to reco…
Collaborative FilteringKnowledge DistillationArtist and style exposure bias in collaborative filtering based music recommendations
Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles. In this on-going work we…
Collaborative FilteringMusic RecommendationBeyond Similarity: Relation Embedding with Dual Attentions for Item-based Recommendation
Given the effectiveness and ease of use, Item-based Collaborative Filtering (ICF) methods have been broadly used in industry in recent years. The key of ICF lies in the similarity measurement between items, which however…
Collaborative FilteringRelationRepresentation LearningvalidFeedback-Based Self-Learning in Large-Scale Conversational AI Agents
Today, most large-scale conversational AI agents (e.g. Alexa, Siri, or Google Assistant) are built using manually annotated data to train the different components of the system. Typically, the accuracy of the ML models i…
Collaborative FilteringSelf-LearningEnhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms
Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders we…
Collaborative FilteringRecommendation SystemsNeural Conversation Recommendation with Online Interaction Modeling
The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis. It presents a concrete challenge for individuals to better discover and engage in social media discussions…
Collaborative FilteringGraph Neural News Recommendation with Long-term and Short-term Interest Modeling
With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include co…
ArticlesCollaborative FilteringNews RecommendationRecommendation Systems