Essays about: "Matrisfaktorisering"
Showing result 1 - 5 of 13 essays containing the word Matrisfaktorisering.
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1. Recommending digital books to children : Acomparative study of different state-of-the-art recommendation system techniques
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Collaborative filtering is a popular technique to use behavior data in the form of user’s interactions with, or ratings of, items in a system to provide personalized recommendations of items to the user. This study compares three different state-of-the-art Recommendation System models that implement this technique, Matrix Factorization, Multi-layer Perceptron and Neural Matrix Factorization, using behavior data from a digital book platform for children. READ MORE
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2. Minimum Cost Distributed Computing using Sparse Matrix Factorization
University essay from KTH/Optimeringslära och systemteoriAbstract : Distributed computing is an approach where computationally heavy problems are broken down into more manageable sub-tasks, which can then be distributed across a number of different computers or servers, allowing for increased efficiency through parallelization. This thesis explores an established distributed computing setting, in which the computationally heavy task involves a number of users requesting a linearly separable function to be computed across several servers. READ MORE
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3. Predicting future purchases with matrix factorization
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This thesis aims to establish the efficacy of using matrix factorization to predict future purchases. Matrix factorisation is a machine learning method, commonly used to implement the collaborative filtering recommendation system. READ MORE
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4. Deep Convolutional Nonnegative Autoencoders
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this thesis, nonnegative matrix factorization (NMF) is viewed as a feedbackward neural network and generalized to a deep convolutional architecture with forwardpropagation under β-divergence. NMF and feedfoward neural networks are put in relation and a new class of autoencoders is proposed, namely the nonnegative autoencoders. READ MORE
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5. Customer segmentation of retail chain customers using cluster analysis
University essay from KTH/Matematisk statistikAbstract : In this thesis, cluster analysis was applied to data comprising of customer spending habits at a retail chain in order to perform customer segmentation. The method used was a two-step cluster procedure in which the first step consisted of feature engineering, a square root transformation of the data in order to handle big spenders in the data set and finally principal component analysis in order to reduce the dimensionality of the data set. READ MORE
