Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
A Neural Attention Model for Adaptive Learning of Social Friends' Preferences
Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge i…
Collaborative FilteringRecommendation SystemsEfficient Neural Interaction Function Search for Collaborative Filtering
In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users. The most popular IFC is the inner product, which has been successfully used in low-ran…
AutoMLCollaborative FilteringModeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
As the core of recommender system, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, and so on. Benefited from the strong repre…
Collaborative FilteringRecommendation SystemsSeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based f…
Collaborative FilteringDeep LearningRecommendation SystemsTemporal SequencesA decentralized trust-aware collaborative filtering recommender system based on weighted items for social tagging systems
Recommender systems are used with the purpose of suggesting contents and resources to the users in a social network. These systems use ranks or tags each user assign to different resources to predict or make suggestions …
Collaborative FilteringRecommendation SystemsTAGExploiting weak ties in trust-based recommender systems using regular equivalence
User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction…
Collaborative FilteringRecommendation SystemsCoupled Variational Recurrent Collaborative Filtering
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstr…
Collaborative FilteringRecommendation SystemsVariational InferenceTowards Amortized Ranking-Critical Training for Collaborative Filtering
Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating flexible representations from deep neural…
Collaborative FilteringLearning-To-RankRecommendation SystemsReinforcement LearningQuaternion Collaborative Filtering for Recommendation
This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Quaternion algebra, benefiting from the ex…
AllCollaborative FilteringInductive BiasRecommendation Systems+1Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
The efficiency of top-K item recommendation based on implicit feedback are vital to recommender systems in real world, but it is very challenging due to the lack of negative samples and the large number of candidate item…
Collaborative FilteringRecommendation SystemsUser Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation
A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find…
Collaborative FilteringMovie RecommendationRecommendation SystemsAssessing Personally Perceived Image Quality via Image Features and Collaborative Filtering
During the past few years, different methods for optimizing the camera settings and post-processing techniques to improve the subjective quality of consumer photos have been studied extensively. However, most of the rese…
Collaborative FilteringRecommendation SystemsWhy Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks
Handling missing data is one of the most fundamental problems in machine learning. Among many approaches, the simplest and most intuitive way is zero imputation, which treats the value of a missing entry simply as zero. …
Collaborative FilteringImputationLeveraging Trust and Distrust in Recommender Systems via Deep Learning
The data scarcity of user preferences and the cold-start problem often appear in real-world applications and limit the recommendation accuracy of collaborative filtering strategies. Leveraging the selections of social fr…
Collaborative FilteringDeep LearningRecommendation SystemsOn the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural Networks
Even in times of deep learning, low-rank approximations by factorizing a matrix into user and item latent factors continue to be a method of choice for collaborative filtering tasks due to their great performance. While …
Collaborative FilteringGraph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we consider recommender systems with side information in the form of graphs. Existing collaborative filtering algorithms mainly utilize only immediate neighborhood information and have a hard time taking a…
Collaborative FilteringRecommendation SystemsA collaborative filtering model with heterogeneous neural networks for recommender systems
In recent years, deep neural network is introduced in recommender systems to solve the collaborative filtering problem, which has achieved immense success on computer vision, speech recognition and natural language proce…
Collaborative FilteringRecommendation Systemsspeech-recognitionSpeech RecognitionEvaluating recommender systems for AI-driven biomedical informatics
Motivation: Many researchers with domain expertise are unable to easily apply machine learning to their bioinformatics data due to a lack of machine learning and/or coding expertise. Methods that have been proposed thus …
AI AgentBIG-bench Machine LearningCollaborative FilteringMeta-Learning+1Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing effor…
Collaborative FilteringLink PredictionRecommendation SystemsCleaned Similarity for Better Memory-Based Recommenders
Memory-based collaborative filtering methods like user or item k-nearest neighbors (kNN) are a simple yet effective solution to the recommendation problem. The backbone of these methods is the estimation of the empirical…
Collaborative Filtering