Sökning: "Collaborative filtering"

Visar resultat 1 - 5 av 105 uppsatser innehållade orden Collaborative filtering.

1. 1. Andra lyssnar även på... : En kvalitativ studie om användarupplevelsen av Spotifys rekommendationssystem.

   Kandidat-uppsats, Umeå universitet/Institutionen för informatik

   Författare :Camilla Fabricio de Barros; Julia Kinnvall; Willmer Pousette Lilja; [2023]
   Nyckelord :Rekommendationssystem; Spotify; användarupplevelse;

   Sammanfattning : The overload of content in digital services demands a way to filter the content for each individual user. The solution to this problem has come to be recommendation systems, which creates recommendations after the behavior patterns and preferences of each user. LÄS MER

3. 2. Context-Aware Fashion Recommender Systems to Provide Intent-based Recommendations to Customers

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Edda Waciira; Marah Thomas; [2023]
   Nyckelord :e-commerce; Fashion Recommendation Systems; Machine Learning; Recommendation Systems;

   Sammanfattning : In recent years, Recommendation Systems have revolutionized how social media and ecommerce are used. Fashion Recommendation Systems have made it easier for customers to do shopping, by recommending items to them based on various factors, such as their previous orders, and their similarities to other users. LÄS MER

4. 3. 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

5. 4. 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

7. 5. Help Document Recommendation System

   Master-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

   Författare :Keerthi Vijay Kumar; Pinky Mary Stanly; [2023]
   Nyckelord :Document similarity; Recommender systems; content-based filtering; collaborative filtering; Term Frequency-Inverse Document Frequency TF-IDF ; Bidirectional Encoder Representation from Transformers BERT ; Non-Negative Matrix Factorisation NMF ; cosine similarity; K-means clustering;

   Sammanfattning : Help documents are important in an organization to use the technology applications licensed from a vendor. Customers and internal employees frequently use and interact with the help documents section to use the applications and know about the new features and developments in them. LÄS MER