Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
A Distributional Representation Model For Collaborative Filtering
In this paper, we propose a very concise deep learning approach for collaborative filtering that jointly models distributional representation for users and items. The proposed framework obtains better performance when co…
Collaborative FilteringmodelCollaborative Filtering Bandits
Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as …
ClusteringCollaborative FilteringNews RecommendationAttention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics
Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation. In this paper, we propose a nov…
Collaborative FilteringA Collaborative Kalman Filter for Time-Evolving Dyadic Processes
We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models. Using the matrix factorization approach to collaborative filtering, the CKF accounts for tim…
Collaborative FilteringDeep Belief Nets for Topic Modeling
Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the oth…
Collaborative FilteringRetrievalA Hybrid Approach to Web Service Recommendation Based on QoS-Aware Rating and Ranking
As the number of Web services with the same or similar functions increases steadily on the Internet, nowadays more and more service consumers pay great attention to the non-functional properties of Web services, also kno…
Collaborative FilteringRecommendation SystemsEfficient Online Relative Comparison Kernel Learning
Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search. For learning a kernel over a large number of objects, e…
Collaborative FilteringRetrievalPredicting User Engagement in Twitter with Collaborative Ranking
Collaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of items to the user. For instance, YouTube …
Collaborative FilteringCollaborative RankingRecommendation SystemsRepresentation Learning for cold-start recommendation
A standard approach to Collaborative Filtering (CF), i.e. prediction of user ratings on items, relies on Matrix Factorization techniques. Representations for both users and items are computed from the observed ratings an…
Collaborative FilteringRepresentation LearningTag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Machine learning offers novel ways and means to design personalized learning systems wherein each student's educational experience is customized in real time depending on their background, learning goals, and performance…
BIG-bench Machine LearningCollaborative FilteringTAGPredicting clicks in online display advertising with latent features and side-information
We review a method for click-through rate prediction based on the work of Menon et al. [11], which combines collaborative filtering and matrix factorization with a side-information model and fuses the outputs to proper p…
Click-Through Rate PredictionCollaborative FilteringPredictionOnline Collaborative-Filtering on Graphs
A common phenomena in modern recommendation systems is the use of feedback from one user to infer the `value' of an item to other users. This results in an exploration vs. exploitation trade-off, in which items of possib…
Collaborative FilteringRecommendation SystemsA Latent Source Model for Online Collaborative Filtering
Despite the prevalence of collaborative filtering in recommendation systems, there has been little theoretical development on why and how well it works, especially in the "online" setting, where items are recommended to …
Collaborative FilteringRecommendation SystemsA Ternary Non-Commutative Latent Factor Model for Scalable Three-Way Real Tensor Completion
Motivated by large-scale Collaborative-Filtering applications, we present a Non-Commuting Latent Factor (NCLF) tensor-completion approach for modeling three-way arrays, which is diagonal like the standard PARAFAC, but wh…
Collaborative FilteringA Parallel and Efficient Algorithm for Learning to Match
Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning…
Collaborative FilteringLink PredictionUsing social network graph analysis for interest detection
A person's interests exist as an internal state and are difficult to define. Since only external actions are observable, a proxy must be used that represents someone's interests. Techniques like collaborative filtering, …
Collaborative Filtering10,000+ Times Accelerated Robust Subset Selection (ARSS)
Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outlie…
10-shot image generationAction RecognitionCollaborative FilteringFine-Grained Image Classification+20Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to mak…
Collaborative FilteringDeep LearningRecommendation SystemsRepresentation LearningConvergence rate of Bayesian tensor estimator: Optimal rate without restricted strong convexity
In this paper, we investigate the statistical convergence rate of a Bayesian low-rank tensor estimator. Our problem setting is the regression problem where a tensor structure underlying the data is estimated. This proble…
Collaborative FilteringMulti-Task LearningregressionMatrix Completion under Interval Uncertainty
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show t…
Collaborative FilteringMatrix Completion