Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Diversification in Session-based News Recommender Systems
Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous and the only available data is sequences o…
Collaborative FilteringDiversityNews RecommendationRecommendation SystemsGraph Attention Collaborative Similarity Embedding for Recommender System
We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning. Our framework co…
Collaborative FilteringGraph AttentionRecommendation SystemsRepresentation LearningFederated Reconstruction: Partially Local Federated Learning
Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client heterogeneity. Approaches that require clients…
Collaborative FilteringFederated LearningMeta-LearningDual-embedding based Neural Collaborative Filtering for Recommender Systems
Among various recommender techniques, collaborative filtering (CF) is the most successful one. And a key problem in CF is how to represent users and items. Previous works usually represent a user (an item) as a vector of…
Collaborative FilteringRecommendation SystemsCausal Collaborative Filtering
Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlative preference. However, pure correlativ…
Collaborative FilteringcounterfactualRecommendation SystemsLeave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system…
Collaborative FilteringRecommendation SystemsMelon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging
One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions of copyrighted commercial music. We pre…
Audio Signal ProcessingCollaborative FilteringInformation RetrievalMetric Learning+4Learning User Preferences in Non-Stationary Environments
Recommendation systems often use online collaborative filtering (CF) algorithms to identify items a given user likes over time, based on ratings that this user and a large number of other users have provided in the past.…
Collaborative FilteringRecommendation SystemsProbabilistic Inference for Learning from Untrusted Sources
Federated learning brings potential benefits of faster learning, better solutions, and a greater propensity to transfer when heterogeneous data from different parties increases diversity. However, because federated learn…
Bayesian InferenceCollaborative FilteringDiversityFederated LearningReinforcement learning based recommender systems: A survey
Recommender systems (RSs) have become an inseparable part of our everyday lives. They help us find our favorite items to purchase, our friends on social networks, and our favorite movies to watch. Traditionally, the reco…
Collaborative FilteringDeep Reinforcement LearningQ-LearningRecommendation Systems+4Measuring Recommender System Effects with Simulated Users
Imagine a food recommender system -- how would we check if it is \emph{causing} and fostering unhealthy eating habits or merely reflecting users' interests? How much of a user's experience over time with a recommender is…
Collaborative FilteringRecommendation SystemsDynamic Graph Collaborative Filtering
Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Base…
Collaborative FilteringRecommendation SystemsApplication of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy
Personalized recommendations are popular in these days of Internet driven activities, specifically shopping. Recommendation methods can be grouped into three major categories, content based filtering, collaborative filte…
ClusteringCollaborative FilteringKnowledge GraphsRecommendation SystemsContrastive Learning for Recommender System
Recommender systems, which analyze users' preference patterns to suggest potential targets, are indispensable in today's society. Collaborative Filtering (CF) is the most popular recommendation model. Specifically, Graph…
Collaborative FilteringContrastive LearningGraph Neural NetworkRecommendation Systems+1Collaborative Filtering with Smooth Reconstruction of the Preference Function
The problem of predicting the rating of a set of users to a set of items in a recommender system based on partial knowledge of the ratings is widely known as collaborative filtering. In this paper, we consider a mapping …
Collaborative FilteringQuantizationRecommendation SystemsRepresentation LearningMulti-Faceted Trust Based Recommendation System
Recommendation systems play a decisive role in the choices we make on the internet. They seek to tailor decisions to a user. This makes trust a very important factor in recommendation systems, since it is believed that u…
Collaborative FilteringRecommendation SystemsInductive Collaborative Filtering via Relation Graph Learning
Collaborative filtering has shown great power in predicting potential user-item ratings by factorizing an observed user-item rating matrix into products of two sets of latent factors. However, the user-specific latent fa…
Collaborative FilteringGraph LearningInductive LearningMatrix Completion+1Untangle: Critiquing Disentangled Recommendations
The core principle behind most collaborative filtering methods is to embed users and items in latent spaces, where individual dimensions are learned independently of any particular item attributes. It is thus difficult f…
Collaborative FilteringExplainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a user. The explanations can be either ob…
Additive modelsCollaborative FilteringExplainable RecommendationRecommendation SystemsA Refined SVD Algorithm for Collaborative Filtering
Collaborative filtering tries to predict the ratings of a user over some items based on opinions of other users with similar taste. The ratings are usually given in the form of a sparse matrix, the goal being to find the…
ClusteringCollaborative Filtering