Sökning: "Personalised Recommendation"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Personalised Recommendation.

1. 1. Streamingtjänster och plattformsbevakning : En studie om musiklivets nya portvakt i en tid av streamad musik

   Kandidat-uppsats, Uppsala universitet/Institutionen för musikvetenskap

   Författare :Philip Grunditz; [2023]
   Nyckelord :gatekeeping; streaming; music curation; algorithms; playlists; digital platforms;

   Sammanfattning : This paper examines how streaming services, with a particular focus on Spotify, have influenced music consumption through the implementation of algorithmically and humanly curated playlists and recommendation features. By placing the role of streaming services in a historical context, this paper explores streaming services as an extension of previous gatekeeping functions in the music industry. LÄS MER

3. 2. Attention based Knowledge Tracing in a language learning setting

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

   Författare :Sebastiaan Vergunst; [2022]
   Nyckelord :Knowledge Tracing; Exercise Recommendation; Personalised Learning; Recurrent Neural Network; Attention; Self-Attention; Exercise Embedding; Kunskapsspårning; Övningsrekommendation; Personligt Anpassad Inlärning; Rekurrenta Neurala Nätverk; Uppmärksamhet; Självuppmärksamhet; Övningsembedding;

   Sammanfattning : Knowledge Tracing aims to predict future performance of users of learning platforms based on historical data, by modeling their knowledge state. In this task, the target is a binary variable representing the correctness of the exercise, where an exercise is a word uttered by the user. LÄS MER

4. 3. Finding time-based listening habits in users music listening history to lower entropy in data

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

   Författare :John Magnusson; [2021]
   Nyckelord :Data mining; Clustering; Listening habits; Entropy.; Datautvinning; Klusteranalys; Lyssningsvanor; Entropi.;

   Sammanfattning : In a world where information, entertainment and e-commerce are growing rapidly in terms of volume and options, it can be challenging for individuals to find what they want. Search engines and recommendation systems have emerged as solutions, guiding the users. LÄS MER

5. 4. Increased Safety on Cycling Paths by Improving Road Maintenance : A Concept to Report Faults and Provide Grades on Cycling Paths

   Master-uppsats, KTH/Hållbarhet och miljöteknik

   Författare :Sara Vitmosse; [2018]
   Nyckelord :MCA; Cycling infrastructure; Operation and maintenance; Mobile application; reporting system;

   Sammanfattning : Urbanisation is increasing and more sustainable transport modes are promoted, in both the sustainable development goals and the national goals. Sweden has developed a national cycling strategy, with the purpose to improve cycling infrastructure and make more people chose the bike. LÄS MER

7. 5. Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation

   Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

   Författare :Oktay Bahceci; [2017]
   Nyckelord :Information Filtering; Information Retrieval; Search Engine; Search Engines; Recommendation; Music Recommendation; Personalized Recommendation; Personalised Recommendation; Context Aware Recommendation; Recommender Systems; Statistical Learning; Artificial Intelligence; Machine Learning; Deep Learning; Neural Networks; Artificial Neural Networks; Feed Forward Neural Networks; Convolutional Neural Networks; Recurrent Neural Networks; Deep Neural Networks; Embedding;

   Sammanfattning : Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. LÄS MER