Sökning: "kallstartsproblemet"

Hittade 3 uppsatser innehållade ordet kallstartsproblemet.

1. 1. Evaluating Cold-Start in Recommendation Systems Using a Hybrid Model Based on Factorization Machines and SBERT Embeddings

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

   Författare :Sabrina Chowdhury; [2022]
   Nyckelord :Natural Language Processing; Hybrid Recommender Systems; Cold-Start Problem; språkteknologi; hybrida rekommendationssystem; kallstartsproblemet;

   Sammanfattning : The item cold-start problem, which describes the difficulty of recommendation systems in recommending new items to users, remains a great challenge for recommendation systems that rely on past user-item interaction data. A popular technique in the current research surrounding the cold-start problem is the use of hybrid models that combine two or more recommendation strategies that may contribute with their individual advantages. LÄS MER

3. 2. 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

4. 3. Recommending new items to customers : A comparison between Collaborative Filtering and Association Rule Mining

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

   Författare :Henrik Sohlberg; [2015]
   Nyckelord :Recommendation system; Association rule mining; Collaborative filtering; Cold start;

   Sammanfattning : E-commerce is an ever growing industry as the internet infrastructure continues to evolve. The benefits from a recommendation system to any online retail store are several. It can help customers to find what they need as well as increase sales by enabling accurate targeted promotions. LÄS MER