Sökning: "matrisfaktorisering"

Visar resultat 1 - 5 av 13 uppsatser innehållade ordet matrisfaktorisering.

1. 1. Recommending digital books to children : Acomparative study of different state-of-the-art recommendation system techniques

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Malvin Lundqvist; [2023]
   Nyckelord :Recommendation Systems; Collaborative Filtering; Matrix Factorization; Multi-Layer Perceptron; Neural Network-based Collaborative Filtering; Implicit Feedback; Deep Learning; Term Frequency-Inverse Document Frequency; Rekommendationssystem; Kollaborativ filtrering; Matrisfaktorisering; Flerlagersperceptron; Neurala nätverksbaserad kollaborativ filtrering; Implicit data; Djupinlärning; Termfrekvens med omvänd dokumentfrekvens;

   Sammanfattning : 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. LÄS MER

3. 2. Minimum Cost Distributed Computing using Sparse Matrix Factorization

   Master-uppsats, KTH/Optimeringslära och systemteori

   Författare :Seif Hussein; [2023]
   Nyckelord :Applied mathematics; optimization; convex optimization; matrix factorization; sparse matrix factorization; distributed computing; linearly separable distributed computing; ADMM; alternating direction method of multipliers; tillämpad matematik; optimering; konvex optimering; matrisfaktorisering; gles matrisfaktorisering; distribuerade beräkningar; admm; alternating direction method of multipliers;

   Sammanfattning : 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. LÄS MER

4. 3. Predicting future purchases with matrix factorization

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Azer Hojlas; August Paulsrud; [2022]
   Nyckelord :Matrix factorisation; machine learning; recommendations systems; Maskininlärning; Matrisfaktorisering; Rekommendationssystem;

   Sammanfattning : 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. LÄS MER

5. 4. Deep Convolutional Nonnegative Autoencoders

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Yann Debain; [2020]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

7. 5. Customer segmentation of retail chain customers using cluster analysis

   Master-uppsats, KTH/Matematisk statistik

   Författare :Sebastian Bergström; [2019]
   Nyckelord :Cluster analysis; customer segmentation; tEIGEN; MCLUST; K-means; NMF; Silhouette; Davies-Bouldin; big spenders; statistics; applied mathematics; unsupervised learning; Klusteranalys; kundsegmentering; tEIGEN; MCLUST; K-means; NMF; Silhouette; Davies-Bouldin; storkonsumenter; statistik; tillämpad matematik;

   Sammanfattning : 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. LÄS MER