Sökning: "kollaborativ filtrering"

Visar resultat 1 - 5 av 12 uppsatser innehållade orden kollaborativ filtrering.

1. 1. Recommender Systems Using Limited Dataset Sizes

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

   Författare :Carl Bentzer; Harry Thulin; [2023]
   Nyckelord :;

   Sammanfattning : In order to create personalized recommendations for users on services such as e-commerce websites and streaming platforms, recommender systems often utilize various machine learning techniques. A common technique used in recommender systems is collaborative filtering which creates rating predictions based on similar users’ interests. LÄS MER

3. 2. 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

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. Recommender system for IT security scanning service : Collaborative filtering in an error report scenario

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

   Författare :Jonas Thunberg; [2022]
   Nyckelord :Collaborative Filtering; Vulnerability Scanning; IT-Security; Recommender System;

   Sammanfattning : Recommender systems have become an integral part of the user interface of many web applications. Recommending items to buy, media to view or similar “next choice”-recommendations has proven to be a powerful tool to improve costumer experience and engagement. LÄS MER

7. 5. New Methodologies for Fashion Recommender Systems

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

   Författare :Gabriele Prato; [2019]
   Nyckelord :;

   Sammanfattning : Traditional Recommender Systems rely on finding similarities between users and/or between items. In its broadest definition, a Recommender System tries to predict the preference a user would give to an item. LÄS MER