Sökning: "Personalized Recommendation"

Visar resultat 1 - 5 av 11 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. Conjugate-Prior-Regularized Multinomial pLSA for Collaborative Filtering

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Marcus Klasson; [2016]
    Nyckelord :Recommender systems; Information filtering; Latent class models; Machine learning; Mathematics and Statistics;

    Sammanfattning : Collaborative filtering is a method for making predictions about consumer interests by collecting preferences or information about opinions from other consumers. For this purpose statistical modeling techniques are applied to learn personalized models for each consumer based on every purchase or provided rating to the available items. LÄS MER

  5. 5. 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