Essays about: "Personalized Recommendation"
Showing result 1 - 5 of 25 essays containing the words Personalized Recommendation.
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1. Exploring the Future of Movie Recommendations : Increasing User Satisfaction using Generative Artificial Intelligence Conversational Agents
University essay from Umeå universitet/Institutionen för tillämpad fysik och elektronikAbstract : 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. READ MORE
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2. Generating an Interpretable Ranking Model: Exploring the Power of Local Model-Agnostic Interpretability for Ranking Analysis
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : 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. READ MORE
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3. Context-Aware Fashion Recommender Systems to Provide Intent-based Recommendations to Customers
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : 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. READ MORE
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4. Personalized Investment Recommendations Using Recommendation Systems
University essay from Lunds universitet/Nationalekonomiska institutionenAbstract : 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. READ MORE
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5. Recommending digital books to children : Acomparative study of different state-of-the-art recommendation system techniques
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE