Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Hypotheses generation using link prediction in a bipartite graph
The large volume of scientific publications is likely to have hidden knowledge that can be used for suggesting new research topics. We propose an automatic method that is helpful for generating research hypotheses in the…
Collaborative FilteringLink PredictionNeural Collaborative Autoencoder
In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of exist…
Collaborative FilteringData AugmentationItem-Item Music Recommendations With Side Information
Online music services have tens of millions of tracks. The content itself is broad and covers various musical genres as well as non-musical audio content such as radio plays and podcasts. The sheer scale and diversity of…
Collaborative FilteringDiversityMixture-Rank Matrix Approximation for Collaborative Filtering
Low-rank matrix approximation (LRMA) methods have achieved excellent accuracy among today's collaborative filtering (CF) methods. In existing LRMA methods, the rank of user/item feature matrices is typically fixed, i.e.,…
Collaborative FilteringThy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation
The sparse matrix estimation problem consists of estimating the distribution of an $n\times n$ matrix $Y$, from a sparsely observed single instance of this matrix where the entries of $Y$ are independent random variable…
Collaborative FilteringCommunity DetectionGraphon EstimationMatrix Completion+1Matrix Norm Estimation from a Few Entries
Singular values of a data in a matrix form provide insights on the structure of the data, the effective dimensionality, and the choice of hyper-parameters on higher-level data analysis tools. However, in many practical a…
Collaborative FilteringMatrix CompletionCollaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, …
Collaborative FilteringRecommendation SystemsLatent Factor Interpretations for Collaborative Filtering
Many machine learning systems utilize latent factors as internal representations for making predictions. Since these latent factors are largely uninterpreted, however, predictions made using them are opaque. Collaborativ…
Collaborative FilteringRecommendation SystemsTowards a More Reliable Privacy-preserving Recommender System
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether. That says, not only the …
Collaborative FilteringPrivacy PreservingRecommendation SystemsSequences, Items And Latent Links: Recommendation With Consumed Item Packs
Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated …
Collaborative FilteringRecommender Systems with Random Walks: A Survey
Recommender engines have become an integral component in today's e-commerce systems. From recommending books in Amazon to finding friends in social networks such as Facebook, they have become omnipresent. Generally, re…
Collaborative FilteringRecommendation SystemsSurveyDeep density networks and uncertainty in recommender systems
Building robust online content recommendation systems requires learning complex interactions between user preferences and content features. The field has evolved rapidly in recent years from traditional multi-arm bandit …
Collaborative FilteringEfficient ExplorationRecommendation SystemsRegret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. Each user may be recommended a given item at most once. A lat…
Collaborative FilteringRecommendation SystemsVocal Bursts Type PredictionPerformance Comparison of Algorithms for Movie Rating Estimation
In this paper, our goal is to compare performances of three different algorithms to predict the ratings that will be given to movies by potential users where we are given a user-movie rating matrix based on the past obse…
Collaborative FilteringEstimating Reactions and Recommending Products with Generative Models of Reviews
Traditional approaches to recommendation focus on learning from large volumes of historical feedback to estimate simple numerical quantities (Will a user click on a product? Make a purchase? etc.). Natural language appro…
Collaborative FilteringLanguage ModelingLanguage ModellingRecommendation Systems+1Online linear optimization with the log-determinant regularizer
We consider online linear optimization over symmetric positive semi-definite matrices, which has various applications including the online collaborative filtering. The problem is formulated as a repeated game between the…
Collaborative FilteringSequential Matrix Completion
We propose a novel algorithm for sequential matrix completion in a recommender system setting, where the $(i,j)$th entry of the matrix corresponds to a user $i$'s rating of product $j$. The objective of the algorithm is …
Collaborative FilteringMatrix CompletionRecommendation SystemsThompson SamplingIs Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems. In recent times, different variants of deep learning algorithms have been explored in this setting to…
ClusteringCollaborative FilteringRecommendation SystemsAlgorithms and Architecture for Real-time Recommendations at News UK
Recommendation systems are recognised as being hugely important in industry, and the area is now well understood. At News UK, there is a requirement to be able to quickly generate recommendations for users on news items …
Collaborative FilteringRecommendation SystemsStarSpace: Embed All The Things!
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
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