150 skills found · Page 5 of 5
CNevd / DiFacto2 FfmDistributed Fieldaware Factorization Machines based on Parameter Server
benedekrozemberczki / GRAFInner product natural graph factorization machine used in 'GEMSEC: Graph Embedding with Self Clustering' .
ADALabUCSD / MorpheusPyFactorized Machine Learning with NumPy
takuti / Factorization Machines:slot_machine: Implementation of Factorization Machines [S. Rendle, 2012]
utkarshsrivastava / ParallelSparseMatrixFactorizationSparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are different approaches to tackle with SMF rooted in linear algebra and probability theory. In this project, given an incomplete binary matrix of students’ performances over a set of questions, estimating the probability of success or fail over unanswered questions is of interest. This problem is formulated using Maximum Likelihood Estimation (MLE) which leads to a biconvex optimization problem (this formulation is based on SPARFA [4]). The resulting optimization problem is a hard problem to deal with due to the existence of many local minima. On the other hand, when the size of the matrix of students’ performances increase, the existing algorithms are not successful; therefore, an efficient algorithm is required to solve this problem for large matrices. In this project, a parallel algorithm (i.e., a parallel version of SPARFA) is developed to solve the biconvex optimization problem and tested via a number of generated matrices. Keywords: parallel non-convex optimization, matrix factorization, sparse factor analysis 1 Introduction Educational systems have witnessed a substantial transition from traditional educational methods mainly using text books, lectures, etc. to newly developed systems which are artificial intelligent- based systems and personally tailored to the learners [4]. Personalized Learning Systems (PLSs) and Intelligent Tutoring Systems (ITSs) are two more well-known instances of such recently developed educational systems. PLSs take into account learners’ individual characteristics then customize the learning experience to the learners’ current situation and needs [2]. As computerized learning environments, ITSs model and track student learning states [1, 6, 7]. Latent Factor Model and Bayesian Knowledge Tracing are main classes in ITSs [3]. These new approaches encompass computational models from different disciplines including cognitive and learning sciences, education, 1 computational linguistics, artificial intelligence, operations research, and other fields. More details can be found in [1, 4–6]. Recently, [4] developed a new machine learning-based model for learning analytics, which approximate a students knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. This model calculates the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each questions intrinsic difficulty [4]. They proposed a bi-convex maximum-likelihood-based solution to the resulting SPARse Factor Analysis (SPARFA) problem. However, the scalability of SPARFA when the number of questions and students significantly increase has not been studied yet.
gaterslebenchen / JLibFFMA Java implementation of LIBFFM: A Library for Field-aware Factorization Machines
MogicianXD / SeqFMA pytorch implementation of SeqFM(Sequence-Aware Factorization Machines for Temporal Predictive Analytics.) [ICDE20]
deepakshankar94 / Movie Recommendation SystemMovie recommendation system built with factorization machines and deep learning
ksanjeevan / Attentional Fm PytorchPyTorch implementation of Attentional Factorization Machines
myeonghak / Mlflow RankfmAn example of MLflow Tracking and Models Using Factorization Machine Recommender model library, rankfm.
rikturr / Mml Feature LearningMiami Machine Learning Meetup - Feature Learning with Matrix Factorization and Neural Networks
SmartTensors / NTFk.jlNonnegative Tensor Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
adamlauretig / Ny R TalkSlides and Code for NY R Users Meetup on January 9, 2020, "A Common Model, Separated by Two Disciplines: Bayesian Factorization Machines with Stan and R" presented by Adam Lauretig
rishabhmisra / Scalable Variational Bayesian Factorization MachineScalable Variational Bayesian inference algorithm for FM is developed which converges faster than the existing state-of-the-art MCMC based inference algorithm. Additionally, a stochastic variational Bayesian algorithm for FM is introduced for large scale learning which utilizes SGD.
hanliu95 / DFMDiscrete Factorization Machines
dselivanov / FMFast Factorization Machines
moriaki3193 / FMLitePython implementation of Factorization Machine
dutta33 / Retail Demand Forecasting Model Using Factorization MachinesIt is challenging to build useful forecasts for sparse demand products. If the forecast is lower than the actual demand, it can lead to poor assortment and replenishment decisions, and customers will not be able to get the products they want when they need them. If the forecast is higher than the actual demand, the unsold products will occupy inventory shelves, and if the products are perishable, they will have to be liquidated at low costs to prevent spoilage. The overall objective of the model is to use the retail data which provides us with historic sales across various countries and products for a firm. We use this information given, and make use of FM’ s to predict the sparse demand with missing transactions. The above step then enhances the overall demand forecast achieved with LSTM analysis. As part of the this project we answered the following questions: How well does matrix factorization perform at predicting intermittent demand How does matrix factorization approach improve the overall time-series forecasting
wofmanaf / FFMField-aware Factorization Machines write in python, train use LBFGS,test in criteo dataset
soominkwon / Machine Learning With Tensor FactorizationsTensor-structured machine learning algorithms (support vector machines, logistic regression, linear regression) named CP-SVM, CP-LogisticRegression, CP-LinearRegression, respectively
