Sökning: "Personalized Recommendation"

Visar resultat 1 - 5 av 9 uppsatser innehållade orden Personalized Recommendation.

  1. 1. 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

  2. 2. Internet of Things (IoT) driven media recommendations for television viewers. The concept of IoT TV

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

    Författare :Trepkeviciute Dovile; [2017]
    Nyckelord :Internet of Things; IoT; Media; Over the Top television; Connected TV; Video recommendations; smart things; ;

    Sammanfattning : In today’s overloaded media landscape, television viewers are constantly confronted with the problem of what media content to select. This media overload speaks directly to the theory of bounded rationality when viewers work to understand all available choices. LÄS MER

  3. 3. Evaluation of memory based collaborative filtering for repository recommendation on Github

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

    Författare :Fredrik Åhs; [2017]
    Nyckelord :;

    Sammanfattning : GitHub is host to a huge number of repositories. In order to explore and find new and interesting repositories on GitHub users has to rely on global charts or explore manually. Recommender systems are a type of software algorithms that produce personalized recommendations to users. LÄS MER

  4. 4. A Continuous Dataflow Pipeline For Low Latency Recommendations

    Master-uppsats, KTH/Skolan för informations- och kommunikationsteknik (ICT)

    Författare :Wu Ge; [2016]
    Nyckelord :;

    Sammanfattning : The goal of building recommender system is to generate personalized recommendations to users. Recommender system has great value in multiple business verticals like video on demand, news, advertising and retailing. In order to recommend to each individual, large number of personal preference data need to be collected and processed. LÄS MER

  5. 5. System för automatiska rekommendationer av nyheter och evenemang

    M1-uppsats, KTH/Data- och elektroteknik

    Författare :Theodor Brandt; [2015]
    Nyckelord :recommendation system; news; events; collaborative filtering; rekommendationssystem; nyheter; evenemang; kollaborativ filtrering;

    Sammanfattning : Teknik och data är nyckeln till att Bonnier Business Media (BBM) ska kunna nå sina mål och leverera ytterligare tillväxt. Därför vill man ligga i framkant när det gäller att undersöka nya tekniker som kan förbättra plattformarna och göra dem mer tidsenliga. LÄS MER