Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation
Frequent group activities of human beings have become an indispensable part in their daily life. Group recommendation can recommend satisfactory activities to group members in the recommender systems, and the key issue i…
Collaborative FilteringDecision MakingRecommendation SystemsRepresentation LearningIncorporating Interpretability into Latent Factor Models via Fast Influence Analysis
Latent factor models (LFMs) such as matrix factorization have achieved the state-of-the-art performance among various collaborative filtering approaches for recommendation. Despite the high recommendation accuracy of LFM…
Collaborative FilteringRobust Max Entrywise Error Bounds for Tensor Estimation from Sparse Observations via Similarity Based Collaborative Filtering
Consider the task of estimating a 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. We introduce a similarity based collaborative filtering algorithm for estima…
Collaborative FilteringMMF: Attribute Interpretable Collaborative Filtering
Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, t…
AttributeCollaborative FilteringRecommendation SystemsScalable Bayesian Non-linear Matrix Completion
Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed. A standard solution is matr…
Collaborative FilteringMatrix CompletionMissing ElementsA difficulty ranking approach to personalization in E-learning
The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differ…
Collaborative FilteringA Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the business by reducing costs incurred due to…
Collaborative FilteringEntity EmbeddingsModeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations
Music recommender systems have become central parts of popular streaming platforms such as Last.fm, Pandora, or Spotify to help users find music that fits their preferences. These systems learn from the past listening ev…
Collaborative FilteringRecommendation SystemsCompleting partial recipes using item-based collaborative filtering to recommend ingredients
Increased public interest in healthy lifestyles has motivated the study of algorithms that encourage people to follow a healthy diet. Applying collaborative filtering to build recommendation systems in domains where only…
Collaborative FilteringDimensionality ReductionRecommendation SystemsCollaborative Filtering and Multi-Label Classification with Matrix Factorization
Machine learning techniques for Recommendation System (RS) and Classification has become a prime focus of research to tackle the problem of information overload. RS are software tools that aim at making informed decision…
ClassificationCollaborative FilteringGeneral ClassificationMatrix Completion+3Neural Cross-Domain Collaborative Filtering with Shared Entities
Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are e…
Collaborative FilteringDiversityRecommendation SystemsEvaluating Recommender System Algorithms for Generating Local Music Playlists
We explore the task of local music recommendation: provide listeners with personalized playlists of relevant tracks by artists who play most of their live events within a small geographic area. Most local artists tend to…
Collaborative FilteringMusic RecommendationRecommendation SystemsHolographic Factorization Machines for Recommendation
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks. FMs are characterized by its usage of the inner product of factorized par…
Collaborative FilteringRetrievalDeep Social Collaborative Filtering
Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative…
Collaborative FilteringRecommendation SystemsJoint Neural Collaborative Filtering for Recommender Systems
We propose a J-NCF method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feat…
Collaborative FilteringRecommendation SystemsAn Item Recommendation Approach by Fusing Images based on Neural Networks
There are rich formats of information in the network, such as rating, text, image, and so on, which represent different aspects of user preferences. In the field of recommendation, how to use those data effectively has b…
Collaborative FilteringRecommendation SystemsA Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images
Throughout the world, breast cancer is one of the leading causes of female death. Recently, deep learning methods are developed to automatically grade breast cancer of histological slides. However, the performance of exi…
Active LearningCollaborative Filteringwhole slide imagesCombating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System
Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend towards predicting a perpetuation of past observed behavior. In a recommendation context, this can lead to an overly narrow set of…
Collaborative FilteringA Review-Driven Neural Model for Sequential Recommendation
Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In cont…
Collaborative FilteringmodelSequential RecommendationAdaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering
Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content…
Collaborative FilteringDeep Learning