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Visar resultat 1 - 5 av 28 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Exploring the Future of Movie Recommendations : Increasing User Satisfaction using Generative Artificial Intelligence Conversational Agents

    Master-uppsats, Umeå universitet/Institutionen för tillämpad fysik och elektronik

    Författare :Signe Bennmarker; [2023]
    Nyckelord :User centered design; Recommender systems; Natural language processing; Conversational agent; User satisfaction;

    Sammanfattning : This thesis explores potential strategies to enhance user control and satisfaction within the movie selection process, with a particular focus on the utilization of conversational generative artificial intelligence, such as ChatGPT, for personalized movie recommendations. The study adopts a qualitative user-centered design thinking approach, aiming to compre-hensively understand user needs, goals, and behavior. LÄS MER

  3. 2. Generating an Interpretable Ranking Model: Exploring the Power of Local Model-Agnostic Interpretability for Ranking Analysis

    Magister-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Laura Galera Alfaro; [2023]
    Nyckelord :Explainable Artificial Intelligence; Learning To Rank; Local ModelAgnostic Interpretability; Ranking Generalized Additive Models;

    Sammanfattning : Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. However, the complexity of learning-to-rank (LTR) models poses challenges in understanding the underlying reasons contributing to the ranking outcomes. LÄS MER

  4. 3. Context-Aware Fashion Recommender Systems to Provide Intent-based Recommendations to Customers

    Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Edda Waciira; Marah Thomas; [2023]
    Nyckelord :e-commerce; Fashion Recommendation Systems; Machine Learning; Recommendation Systems;

    Sammanfattning : In recent years, Recommendation Systems have revolutionized how social media and ecommerce are used. Fashion Recommendation Systems have made it easier for customers to do shopping, by recommending items to them based on various factors, such as their previous orders, and their similarities to other users. LÄS MER

  5. 4. Personalized Investment Recommendations Using Recommendation Systems

    Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen

    Författare :Lorik Sadriu; [2023]
    Nyckelord :Recommendation systems; Deep learning; Institutional investors; Investment decision-making; Mean-variance spanning test; Cross-Selling; Business and Economics;

    Sammanfattning : This paper presents a Deep Learning-based Hybrid Recommendation System (DLHR) designed specifically for institutional investors with public portfolio holdings on the Stockholm Stock Exchange. The objective is to provide personalized investment recommendations, complement existing portfolios, and explore untapped cross-selling opportunities. LÄS MER

  7. 5. Recommending digital books to children : Acomparative study of different state-of-the-art recommendation system techniques

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

    Författare :Malvin Lundqvist; [2023]
    Nyckelord :Recommendation Systems; Collaborative Filtering; Matrix Factorization; Multi-Layer Perceptron; Neural Network-based Collaborative Filtering; Implicit Feedback; Deep Learning; Term Frequency-Inverse Document Frequency; Rekommendationssystem; Kollaborativ filtrering; Matrisfaktorisering; Flerlagersperceptron; Neurala nätverksbaserad kollaborativ filtrering; Implicit data; Djupinlärning; Termfrekvens med omvänd dokumentfrekvens;

    Sammanfattning : Collaborative filtering is a popular technique to use behavior data in the form of user’s interactions with, or ratings of, items in a system to provide personalized recommendations of items to the user. This study compares three different state-of-the-art Recommendation System models that implement this technique, Matrix Factorization, Multi-layer Perceptron and Neural Matrix Factorization, using behavior data from a digital book platform for children. LÄS MER