Sökning: "sparse matrix factorization"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden sparse matrix factorization.

1. 1. 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

3. 2. Switching hybrid recommender system to aid the knowledge seekers

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Alexander Backlund; [2020]
   Nyckelord :ML; Machine Learning; Recommender System; Information Filtering; Content-Based Filtering; Collaborative Filtering; Hybrid Filtering; KNN; MF; Matrix Factorization;

   Sammanfattning : In our daily life, time is of the essence. People do not have time to browse through hundreds of thousands of digital items every day to find the right item for them. This is where a recommendation system shines. Tigerhall is a company that distributes podcasts, ebooks and events to subscribers. LÄS MER

4. 3. Recommender System for Gym Customers

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Roshni Sundaramurthy; [2020]
   Nyckelord :Recommender system; collaborative filtering; matrix factorization; sparse matrix; latent semantic analysis; singular value decomposition; alternating least square; Bayesian personalized ranking; logistic matrix factorization; stochastic gradient descent; AUC metric; mean average precision; normalized discounted cumulative gain; Rekommendationssystem;

   Sammanfattning : Recommender systems provide new opportunities for retrieving personalized information on the Internet. Due to the availability of big data, the fitness industries are now focusing on building an efficient recommender system for their end-users. This thesis investigates the possibilities of building an efficient recommender system for gym users. LÄS MER

5. 4. Improving Food Recipe Suggestions with Hierarchical Classification of Food Recipes

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

   Författare :Pedram Fathollahzadeh; [2018]
   Nyckelord :collaborative filtering; content-based method; matrix factorization; recommender systems; hierarchical classification; food recipes;

   Sammanfattning : Making personalized recommendations has become a central part in many platforms, and is continuing to grow with more access to massive amounts of data online. Giving recommendations based on the interests of the individual, rather than recommending items that are popular, increases the user experience and can potentially attract more customers when done right. LÄS MER

7. 5. Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?

   Master-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Zakris Strömqvist; [2018]
   Nyckelord :Recommender systems; Collaborative filtering; Matrix factorization;

   Sammanfattning : One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. LÄS MER