Sökning: "Neural Collaborative Filtering"

Visar resultat 1 - 5 av 10 uppsatser innehållade orden Neural Collaborative Filtering.

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. Comparison of state-of-the-art Temporal Interaction Network methods in different settings : Novel models to predict temporal behavior

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

   Författare :Indre Tauroseviciute; [2021]
   Nyckelord :Recommendation systems; Neural Collaborative Filtering; RNN; Backpropagation; Comparative analysis;

   Sammanfattning : Recommendation systems become more and more necessary due to the growing supply chain. Therefore, scientists are developing models that can serve different recommendation needs faster than before, and it is getting more complicated to choose the model for a specific case. LÄS MER

4. 3. A Recommender System for Suggested Sites using Multi-Armed Bandits : Initialising Bandit Contexts by Neural Collaborative Filtering

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :William Stenberg; [2021]
   Nyckelord :Recommender Systems; Neural Collaborative Filtering; Multi-Armed Bandits;

   Sammanfattning : The abundance of information available on the internet necessitates means of quickly finding what is relevant for the individual user. To this end, there has been much research concerning recommender systems and lately specifically methods using deep learning for such systems. LÄS MER

5. 4. Federated Neural Collaborative Filtering for privacy-preserving recommender systems

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

   Författare :Johannes Langelaar; Adam Strömme Mattsson; [2021]
   Nyckelord :Machine learning; Federated learning; Deep learning; Artificial intelligence; AI; Neural networks; Recommender systems; Recommendation systems; Collaborative filtering; Privacy; Federated averaging; Movielens;

   Sammanfattning : In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. LÄS MER

7. 5. Automatic Music Recommendation for Businesses : Using a two-stage Membership model for track recommendation

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

   Författare :Svante Haapanen Rollenhagen; [2021]
   Nyckelord :Deep Learning; Music Recommendation; Recommendation Systems; Music Informatics.; Djupinlärning; Musikrekommendation; Rekommendationssystem; Musikinformatik.;

   Sammanfattning : This thesis proposes a two-stage recommendation system for providing music recommendations based on seed playlists as inputs. The goal is to help businesses find relevant and brand-fit music to play in their venues. LÄS MER