Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation
Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and explore users' engagement and maximize th…
Collaborative FilteringRecommendation SystemsThompson SamplingAccelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
The matrix completion problem seeks to recover a $d\times d$ ground truth matrix of low rank $r\ll d$ from observations of its individual elements. Real-world matrix completion is often a huge-scale optimization problem,…
Collaborative FilteringMatrix CompletionDynamic Causal Collaborative Filtering
Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of rec…
Collaborative FilteringcounterfactualCounterfactual ReasoningRecommendation SystemsRevisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective
Graph neural networks (GNN) based collaborative filtering (CF) have attracted increasing attention in e-commerce and social media platforms. However, there still lack efforts to evaluate the robustness of such CF systems…
Collaborative FilteringHySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI) recommendations and computing resource…
Collaborative FilteringGraph EmbeddingLFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation
Tag-aware recommendation is a task of predicting a personalized list of items for a user by their tagging behaviors. It is crucial for many applications with tagging capabilities like last.fm or movielens. Recently, many…
Collaborative FilteringRecommendation SystemsTAGGeometric Interaction Augmented Graph Collaborative Filtering
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests. Conventionally, the embedd…
Collaborative FilteringSelf-Supervised Hypergraph Transformer for Recommender Systems
Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN-based recommender systems is to recursi…
Collaborative FilteringData AugmentationRecommendation SystemsSelf-Supervised LearningHICF: Hyperbolic Informative Collaborative Filtering
Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage …
Collaborative FilteringRecommendation SystemsEnhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis
Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. However, CF recommender has been proven to suffer from persistent problems related to sparsity of the user rating that …
Collaborative FilteringSentiment AnalysisLearning Sparsity-Promoting Regularizers using Bilevel Optimization
We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images. Sparsity-promoting regularization is a key ingredient in solving modern signal reconstruction problems; how…
Bilevel OptimizationCollaborative FilteringDenoisingDictionary Learning+1Flow Moods: Recommending Music by Moods on Deezer
The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. Nonetheless, despite promising results ove…
Collaborative FilteringFine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationsh…
Collaborative FilteringData Augmentationgraph partitioningGraph Sampling+1On the Relationship Between Counterfactual Explainer and Recommender
Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative filtering (NCF) are increasingly popular…
Collaborative FilteringcounterfactualCounterfactual ExplanationRecommendation SystemsInteractive Recommendations for Optimal Allocations in Markets with Constraints
Recommendation systems when employed in markets play a dual role: they assist users in selecting their most desired items from a large pool and they help in allocating a limited number of items to the users who desire th…
Collaborative FilteringRecommendation SystemsModelling Users with Item Metadata for Explainable and Interactive Recommendation
Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as "connecting relevant content to interested users". Personalized recommendation algorithms achi…
Collaborative FilteringInteractive RecommendationRecommendation SystemsItem Recommendation Using User Feedback Data and Item Profile
Matrix factorization (MS) is a collaborative filtering (CF) based approach, which is widely used for recommendation systems (RS). In this research work, we deal with the content recommendation problem for users in a cont…
Collaborative FilteringManagementRecommendation SystemsTowards Representation Alignment and Uniformity in Collaborative Filtering
Collaborative filtering (CF) plays a critical role in the development of recommender systems. Most CF methods utilize an encoder to embed users and items into the same representation space, and the Bayesian personalized …
Collaborative FilteringGraph Neural NetworkRecommendation SystemsA Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System
Currently, matrix decomposition is one of the most widely used collaborative filtering algorithms by using factor decomposition to effectively deal with large-scale rating matrix. It mainly uses the interaction records b…
AttributeCollaborative FilteringMovie RecommendationRecommendation SystemsUnlearning Protected User Attributes in Recommendations with Adversarial Training
Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, race, and location. These encoded biases …
Collaborative Filtering