Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Matrix factorization (MF) has been widely applied to collaborative filtering in recommendation systems. Its Bayesian variants can derive posterior distributions of user and item embeddings, and are more robust to sparse …
Collaborative FilteringRecommendation SystemsHotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset
Today, recommender systems are an inevitable part of everyone's daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models require a large number of data to achieve thei…
Collaborative FilteringRecommendation SystemsTensor denoising and completion based on ordinal observations
Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies. We consider the problem of low-rank tensor estimation from possibly i…
ClusteringCollaborative FilteringDenoisingTowards an Appropriate Query, Key, and Value Computation for Knowledge Tracing
Knowledge tracing, the act of modeling a student's knowledge through learning activities, is an extensively studied problem in the field of computer-aided education. Although models with attention mechanism have outperfo…
Collaborative FilteringDecoderKnowledge TracingLearnable Bernoulli Dropout for Bayesian Deep Learning
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters. By probabilistic modeling of B…
Collaborative FilteringDeep Learningimage-classificationImage Classification+2LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to rec…
Collaborative FilteringGraph ClassificationMulti-modal RecommendationRecommendation SystemsQuantifying the Effects of Recommendation Systems
Recommendation systems today exert a strong influence on consumer behavior and individual perceptions of the world. By using collaborative filtering (CF) methods to create recommendations, it generates a continuous feedb…
Collaborative FilteringRecommendation SystemsGraph Convolution Machine for Context-aware Recommender System
The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the …
Collaborative FilteringDecoderRecommendation SystemsRevisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Rece…
Collaborative FilteringRecommendation SystemsRepresentation LearningPredicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach
Experimental corn hybrids are created in plant breeding programs by crossing two parents, so-called inbred and tester, together. Identification of best parent combinations for crossing is challenging since the total numb…
Collaborative FilteringHybrid Semantic Recommender System for Chemical Compounds
Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Re…
Collaborative FilteringRecommendation SystemsSemantic SimilaritySemantic Textual SimilarityUnifying paragraph embeddings and neural collaborative filtering for hybrid recommendation
Collaborative filtering is one of widely used recommendation techniques. Despite the effectiveness of matrix factorization for collaborative filtering; however, the inner product operator, combining the multiplication …
Collaborative FilteringDiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early…
Collaborative FilteringBasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
Within-basket recommendation reduces the exploration time of users, where the user's intention of the basket matters. The intent of a shopping basket can be retrieved from both user-item collaborative filtering signals a…
Collaborative FilteringLink PredictionA Correspondence Analysis Framework for Author-Conference Recommendations
For many years, achievements and discoveries made by scientists are made aware through research papers published in appropriate journals or conferences. Often, established scientists and especially newbies are caught up …
Collaborative FilteringDimensionality ReductionMulti-Graph Convolution Collaborative Filtering
Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with the goal of predicting a us…
Collaborative FilteringGraph Neural NetworkSequential Recommender Systems: Challenges, Progress and Prospects
The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, S…
Collaborative FilteringRecommendation SystemsRecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which used the multinomial likelihood variati…
Collaborative FilteringRecommendation SystemsOnline Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young
In this paper, we analyze a large dataset of user-generated music listening events from Last.fm, focusing on users aged 6 to 18 years. Our contribution is two-fold. First, we study the music genre preferences of this you…
Collaborative FilteringCultural Vocal Bursts Intensity PredictionMusic RecommendationAn Explainable Autoencoder For Collaborative Filtering Recommendation
Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems t…
Collaborative FilteringDeep LearningExplainable RecommendationRecommendation Systems+1