Sökning: "rekommendationssystem"

Visar resultat 1 - 5 av 106 uppsatser innehållade ordet rekommendationssystem.

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. Designing Diverse Features to Reduce the Filter Bubble Effect on Social Media

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

   Författare :Ramya Kandula; [2023]
   Nyckelord :Filter bubbles; recommender systems; diversity; social media; filter bubblor; rekommenderande system; mångfald; sociala medier;

   Sammanfattning : The filter bubble effect has been an active area of research that has been explored in various contexts within social media. Research on recommender system designs within filter bubbles has received a lot of attention, mainly due to its impact on the phenomena. 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. Queryable Workflows: Extending Dataflow Streaming with Dynamic Request/Reply Communication

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

   Författare :Chengyang Huang; [2023]
   Nyckelord :Stream Processing; Observability; SQL Query Engine; Stateful Serverless; Searbetning av Strömmar; Observabilitet; SQL-förfrågningsmotor; Stateful Serverless;

   Sammanfattning : Stream processing systems have been widely adopted in applications such as recommendation systems, anomaly detection, and system monitoring due to their real-time capabilities. Improving observability in stream processing systems can further expand their application scenarios, including the implementation of stateful serverless applications. LÄS MER