Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations
With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts. In this work, we present a novel, explainable, topical re…
Collaborative FilteringRecommendation SystemsVariational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems
Factorization machines (FMs) are a powerful tool for regression and classification in the context of sparse observations, that has been successfully applied to collaborative filtering, especially when side information ov…
Active LearningCollaborative FilteringRecommendation SystemsCausal Inference for Knowledge Graph based Recommendation
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model. By mapping items with the entities in KGs, prior studies mostly extract the kno…
Causal InferenceCollaborative FilteringcounterfactualCounterfactual InferenceCOLA: Improving Conversational Recommender Systems by Collaborative Augmentation
Conversational recommender systems (CRS) aim to employ natural language conversations to suggest suitable products to users. Understanding user preferences for prospective items and learning efficient item representation…
CoLACollaborative FilteringRecommendation SystemsRepresentation LearningInteractive Learning with Pricing for Optimal and Stable Allocations in Markets
Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback. As a principled way of incorporating market…
Collaborative FilteringRecommendation SystemsMovie Recommendation System using Composite Ranking
In today's world, abundant digital content like e-books, movies, videos and articles are available for consumption. It is daunting to review everything accessible and decide what to watch next. Consequently, digital medi…
ArticlesCollaborative FilteringMovie RecommendationRecommendation SystemsEnhancing Recommender Systems: A Strategy to Mitigate False Negative Impact
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs). Effective and efficient negative samp…
Collaborative FilteringRecommendation SystemsA Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise Consistency
Matrix completion is a class of machine learning methods that concerns the prediction of missing entries in a partially observed matrix. This paper studies matrix completion for mixed data, i.e., data involving mixed typ…
Collaborative FilteringMatrix CompletionBlurring-Sharpening Process Models for Collaborative Filtering
Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods. Bei…
Collaborative FilteringRecommendation SystemsUser-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity
Collaborative Filtering (CF), the most common approach to build Recommender Systems, became pervasive in our daily lives as consumers of products and services. However, challenges limit the effectiveness of Collaborative…
Collaborative FilteringDiversityRecommendation SystemsConversion-Based Dynamic-Creative-Optimization in Native Advertising
Yahoo Gemini native advertising marketplace serves billions of impressions daily, to hundreds millions of unique users, and reaches a yearly revenue of many hundreds of millions USDs. Powering Gemini native models for pr…
AttributeCollaborative FilteringTimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically …
Collaborative FilteringRecommendation SystemsTime SeriesTime Series Analysis+1Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering
Abstract—Nowadays, there is a large number of web ser-vices with similar functions, from which users choose the best according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service rec…
Collaborative FilteringPredictionIncorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences. However, most current debiasing strategies are prone to playing a trade-off game …
Collaborative FilteringMDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies
Due to the success of graph convolutional networks (GCNs) in effectively extracting features in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering. Existing works, while encouraging…
Collaborative FilteringGraph Representation LearningRecommendation SystemsRepresentation LearningMEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user pref…
Collaborative Filteringimage-classificationImage ClassificationSapling Similarity: a performing and interpretable memory-based tool for recommendation
Many bipartite networks describe systems where an edge represents a relation between a user and an item. Measuring the similarity between either users or items is the basis of memory-based collaborative filtering, a wide…
Collaborative FilteringRecommendation SystemsMulti-User Reinforcement Learning with Low Rank Rewards
In this work, we consider the problem of collaborative multi-user reinforcement learning. In this setting there are multiple users with the same state-action space and transition probabilities but with different rewards.…
Collaborative Filteringreinforcement-learningReinforcement LearningReinforcement Learning (RL)FusionDeepMF: A Dual Embedding based Deep Fusion Model for Recommendation
Traditional Collaborative Filtering (CF) based methods are applied to understand the personal preferences of users/customers for items or products from the rating matrix. Usually, the rating matrix is sparse in nature. S…
Collaborative FilteringJoint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation
Graph Convolution Networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple neighbor aggregation is the major operation…
Collaborative FilteringRecommendation SystemsSensitivity