Sökning: "Content-based filtering"

Visar resultat 11 - 15 av 35 uppsatser innehållade orden Content-based filtering.

1. 11. A Comparison between Different Recommender System Approaches for a Book and an Author Recommender System

   Master-uppsats, Linköpings universitet/Interaktiva och kognitiva system

   Författare :Jesper Hedlund; Emma Nilsson Tengstrand; [2020]
   Nyckelord :Recommender System; Collaborative Filter; Matrix Factorization; Content-based Filter; Doc2Vec;

   Sammanfattning : A recommender system is a popular tool used by companies to increase customer satisfaction and to increase revenue. Collaborative filtering and content-based filtering are the two most common approaches when implementing a recommender system, where the former provides recommendations based on user behaviour, and the latter uses the characteristics of the items that are recommended. LÄS MER

3. 12. Switching hybrid recommender system to aid the knowledge seekers

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Alexander Backlund; [2020]
   Nyckelord :ML; Machine Learning; Recommender System; Information Filtering; Content-Based Filtering; Collaborative Filtering; Hybrid Filtering; KNN; MF; Matrix Factorization;

   Sammanfattning : In our daily life, time is of the essence. People do not have time to browse through hundreds of thousands of digital items every day to find the right item for them. This is where a recommendation system shines. Tigerhall is a company that distributes podcasts, ebooks and events to subscribers. LÄS MER

4. 13. Impact of implicit data in a job recommender system

   Kandidat-uppsats, Jönköping University/JTH, Datateknik och informatik

   Författare :Josef Wakman; [2020]
   Nyckelord :Recommender systems; Prediction; Job recommendation; Content based filtering; Implicit data; Explicit data; User behavior.;

   Sammanfattning : Many employment services base their online job recommendations to users based solely on explicit data in their profiles. The implicit data of what users for example click on, save and mark as irrelevant goes unused. Instead of making recommendations based on user behavior they make a direct comparison between user preferences and job ad attributes. LÄS MER

5. 14. Music Recommendations; Approximating user distributions to address the cold start problem

   Kandidat-uppsats, Lunds universitet/Statistiska institutionen

   Författare :Johan Hammarstedt; [2020]
   Nyckelord :Music recommendation Content based filtering Collaborative filtering Matrix factorization user-feature distribution; Technology and Engineering;

   Sammanfattning : In today's data driven society the world is at a point of information overload. As people rely on Google for information and other platforms such as Netflix and Spotify for entertainment, the need for relevant filtering of content has never been higher. As a result, recommendation systems have seen a great surge in demand. LÄS MER

7. 15. New Methodologies for Fashion Recommender Systems

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

   Författare :Gabriele Prato; [2019]
   Nyckelord :;

   Sammanfattning : Traditional Recommender Systems rely on finding similarities between users and/or between items. In its broadest definition, a Recommender System tries to predict the preference a user would give to an item. LÄS MER