Papers Collaborative Filtering
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Hybrid Recommender System Based on Personal Behavior Mining
Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied i…
Collaborative FilteringRecommendation SystemsSequential Pattern MiningST-MVL: Filling Missing Values in Geo-Sensory Time Series Data
Many sensors have been deployed in the physical world, generating massive geo-tagged time series data. In reality, readings of sensors are usually lost at various unexpected moments because of sensor or communication err…
Collaborative FilteringImputationMissing ValuesMultivariate Time Series Imputation+3Tensor Decomposition for Signal Processing and Machine Learning
Tensors or {\em multi-way arrays} are functions of three or more indices $(i,j,k,\cdots)$ -- similar to matrices (two-way arrays), which are functions of two indices $(r,c)$ for (row,column). Tensors have a rich history,…
BIG-bench Machine LearningCollaborative FilteringTensor DecompositionApplication of Statistical Relational Learning to Hybrid Recommendation Systems
Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item …
Collaborative FilteringFeature EngineeringRecommendation SystemsRelational ReasoningMemory Based Collaborative Filtering with Lucene
Memory Based Collaborative Filtering is a widely used approach to provide recommendations. It exploits similarities between ratings across a population of users by forming a weighted vote to predict unobserved ratings. B…
Collaborative FilteringHybrid Recommender System based on Autoencoders
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts wh…
Collaborative FilteringMatrix CompletionMissing ValuesRecommendation SystemsExplainable Restricted Boltzmann Machines for Collaborative Filtering
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefi…
Collaborative FilteringExplanation GenerationRecommendation SystemsLLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios
The purpose if this master's thesis is to study and develop a new algorithmic framework for Collaborative Filtering to produce recommendations in the top-N recommendation problem. Thus, we propose Lanczos Latent Factor R…
Collaborative FilteringRecommendation SystemsExact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Example…
BIG-bench Machine LearningCollaborative FilteringInformation RetrievalMulti-Label Classification+3Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method
Pairwise learning or dyadic prediction concerns the prediction of properties for pairs of objects. It can be seen as an umbrella covering various machine learning problems such as matrix completion, collaborative filteri…
Collaborative FilteringMatrix CompletionMulti-Task LearningPrediction+3Regret Bounds for Non-decomposable Metrics with Missing Labels
We consider the problem of recommending relevant labels (items) for a given data point (user). In particular, we are interested in the practically important setting where the evaluation is with respect to non-decomposabl…
Collaborative FilteringMissing LabelsA Neural Autoregressive Approach to Collaborative Filtering
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribut…
Collaborative FilteringMatrix Completion has No Spurious Local Minimum
Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in prac…
Collaborative FilteringMatrix CompletionRecommendation SystemsCollaborative Filtering with Side Information: a Gaussian Process Perspective
We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we for…
Collaborative FilteringregressionDictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising. Its applicability to large datasets has been addressed with onl…
Collaborative FilteringDictionary LearningMatrix CompletionRecommendation SystemsDynamic matrix factorization with social influence
Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief th…
Collaborative FilteringRecommendation SystemsSherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining u…
Collaborative FilteringRecommendation Systems1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation
Due to challenging applications such as collaborative filtering, the matrix completion problem has been widely studied in the past few years. Different approaches rely on different structure assumptions on the matrix in …
Collaborative FilteringLearning TheoryMatrix CompletionHierarchical Compound Poisson Factorization
Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering …
Collaborative FilteringRecommendation SystemsAccurate and scalable social recommendation using mixed-membership stochastic block models
With ever-increasing amounts of online information available, modeling and predicting individual preferences-for books or articles, for example-is becoming more and more important. Good predictions enable us to improve a…
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