Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses
Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is …
Collaborative FilteringDimensionality ReductionTensor Methods and Recommender Systems
A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the bo…
Collaborative FilteringRecommendation SystemsItem2Vec: Neural Item Embedding for Collaborative Filtering
Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (N…
Collaborative FilteringHybrid Collaborative Filtering with Autoencoders
Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side…
Collaborative Filteringspeech-recognitionOn Tie Strength Augmented Social Correlation for Inferring Preference of Mobile Telco Users
For mobile telecom operators, it is critical to build preference profiles of their customers and connected users, which can help operators make better marketing strategies, and provide more personalized services. With th…
Collaborative FilteringMarketingLatent Skill Embedding for Personalized Lesson Sequence Recommendation
Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic…
Collaborative FilteringRecommendation SystemsRecovering Structured Probability Matrices
We consider the problem of accurately recovering a matrix B of size M by M , which represents a probability distribution over M2 outcomes, given access to an observed matrix of "counts" generated by taking independent sa…
Collaborative FilteringCommunity DetectionRecommendation SystemsTopic Models+1Personalized Expertise Search at LinkedIn
LinkedIn is the largest professional network with more than 350 million members. As the member base increases, searching for experts becomes more and more challenging. In this paper, we propose an approach to address the…
Collaborative FilteringCollaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users…
Collaborative FilteringMovie RecommendationRecommendation SystemsUps and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly di…
Collaborative FilteringRecommendation SystemsA Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering
In this paper, we present a theoretical framework for tackling the cold-start collaborative filtering problem, where unknown targets (items or users) keep coming to the system, and there is a limited number of resources …
Collaborative FilteringOnline Prediction of Dyadic Data with Heterogeneous Matrix Factorization
Dyadic Data Prediction (DDP) is an important problem in many research areas. This paper develops a novel fully Bayesian nonparametric framework which integrates two popular and complementary approaches, discrete mixed me…
Collaborative FilteringVariational InferenceImplicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation
User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users' interest profiles vi…
Collaborative FilteringTransfer LearningAlgorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark
Collaborative filtering algorithms are important building blocks in many practical recommendation systems. For example, many large-scale data processing environments include collaborative filtering models for which the A…
Collaborative FilteringRecommendation SystemsSong Recommendation with Non-Negative Matrix Factorization and Graph Total Variation
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variati…
Collaborative FilteringMatrix CompletionRecommendation SystemsFast Kronecker product kernel methods via generalized vec trick
Kronecker product kernel provides the standard approach in the kernel methods literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations. The m…
Collaborative FilteringInformation RetrievalRetrievalA Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case
The tripartite graph is one of the commonest topological structures in social tagging systems such as Delicious, which has three types of nodes (i.e., users, URLs and tags). Traditional recommender systems developed base…
ClusteringCollaborative FilteringCPURecommendation SystemsExploiting Hierarchy for Ranking-based Recommendation
The purpose of this master's thesis is to study and develop a new algorithmic framework for collaborative filtering (CF) to generate recommendations. The method we propose is based on the exploitation of the hierarchical…
Collaborative FilteringCollaborative Filtering for Predicting User Preferences for Organizing Objects
As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, different users typically have their own pre…
Collaborative FilteringCommon Sense ReasoningExplaining reviews and ratings with PACO: Poisson Additive Co-Clustering
Understanding a user's motivations provides valuable information beyond the ability to recommend items. Quite often this can be accomplished by perusing both ratings and review texts, since it is the latter where the rea…
ClusteringCollaborative Filtering