Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning
Wikipedia is edited by volunteer editors around the world. Considering the large amount of existing content (e.g. over 5M articles in English Wikipedia), deciding what to edit next can be difficult, both for experienced …
ArticlesCollaborative FilteringGraph EmbeddingRepresentation LearningA Deep Hybrid Model for Recommendation Systems
Recommendation has been a long-standing problem in many areas ranging from e-commerce to social websites. Most current studies focus only on traditional approaches such as content-based or collaborative filtering while t…
Collaborative FilteringDeep LearningmodelRecommendation SystemsHotel Recommendation System Based on User Profiles and Collaborative Filtering
Nowadays, people start to use online reservation systems to plan their vacations since they have vast amount of choices available. Selecting when and where to go from this large-scale options is getting harder. In additi…
Collaborative FilteringDecision MakingRecommendation SystemsUniNet: Next Term Course Recommendation using Deep Learning
Course enrollment recommendation is a relevant task that helps university students decide what is the best combination of courses to enroll in the next term. In particular, recommender system techniques like matrix facto…
Collaborative FilteringDeep LearningRecommendation SystemsContent Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting
Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the data sets include features that describ…
Collaborative FilteringRecommendation SystemsRule-Guided Graph Neural Networks for Recommender Systems
To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources. However, existing work incorporated w…
Collaborative FilteringKnowledge GraphsRecommendation SystemsTrajectory Based Podcast Recommendation
Podcast recommendation is a growing area of research that presents new challenges and opportunities. Individuals interact with podcasts in a way that is distinct from most other media; and primary to our concerns is dist…
Collaborative FilteringImplicit Feedback Deep Collaborative Filtering Product Recommendation System
In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviours. T…
Bayesian OptimizationCollaborative FilteringProduct RecommendationSimplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data. As two major concerns in negative sampling, efficiency and effectiveness are still…
Collaborative FilteringEfficient Model-Based Collaborative Filtering with Fast Adaptive PCA
A model-based collaborative filtering (CF) approach utilizing fast adaptive randomized singular value decomposition (SVD) is proposed for the matrix completion problem in recommender system. Firstly, a fast adaptive PCA …
Collaborative FilteringMatrix CompletionRecommendation SystemsExploring Artist Gender Bias in Music Recommendation
Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' …
Collaborative FilteringMusic RecommendationRecommendation SystemsNeural Fair Collaborative Filtering
A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In…
Collaborative FilteringDecision MakingRecommendation SystemsDual Channel Hypergraph Collaborative Filtering
Collaborative filtering (CF) is one of the most popular and important recommendation methodologies in the heart of numerous recommender systems today. Although widely adopted, existing CF-based methods, ranging from mat…
Collaborative FilteringRecommendation SystemsTime-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach
Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest …
Collaborative FilteringRecommendation SystemsA Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to the serious concerns over user privacy an…
Collaborative FilteringFederated LearningPrivacy PreservingCollaborative Filtering under Model Uncertainty
In their work, Dean, Rich, and Recht create a model to research recourse and availability of items in a recommender system. We used the definition of predictive multiplicity by Marx, Pin Calmon, and Ustun to examine diff…
Collaborative FilteringmodelRecommendation SystemsTheoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their effect on the recommenda…
BIG-bench Machine LearningCollaborative FilteringRecommendation SystemsReview Regularized Neural Collaborative Filtering
In recent years, text-aware collaborative filtering methods have been proposed to address essential challenges in recommendations such as data sparsity, cold start problem, and long-tail distribution. However, many of th…
Collaborative FilteringE-commerce Recommendation with Weighted Expected Utility
Different from shopping at retail stores, consumers on e-commerce platforms usually cannot touch or try products before purchasing, which means that they have to make decisions when they are uncertain about the outcome (…
Collaborative FilteringRecommendation SystemsLearning the Structure of Auto-Encoding Recommenders
Autoencoder recommenders have recently shown state-of-the-art performance in the recommendation task due to their ability to model non-linear item relationships effectively. However, existing autoencoder recommenders use…
Collaborative Filtering