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Showing result 1 - 5 of 94 essays matching the above criteria.

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

    University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

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

    Abstract : 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

  3. 2. Recommender Systems Using Limited Dataset Sizes

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Carl Bentzer; Harry Thulin; [2023]
    Keywords : ;

    Abstract : In order to create personalized recommendations for users on services such as e-commerce websites and streaming platforms, recommender systems often utilize various machine learning techniques. A common technique used in recommender systems is collaborative filtering which creates rating predictions based on similar users’ interests. READ MORE

  4. 3. 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)

    Author : Malvin Lundqvist; [2023]
    Keywords : 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;

    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

  5. 4. Help Document Recommendation System

    University essay from Malmö universitet/Fakulteten för teknik och samhälle (TS)

    Author : Keerthi Vijay Kumar; Pinky Mary Stanly; [2023]
    Keywords : Document similarity; Recommender systems; content-based filtering; collaborative filtering; Term Frequency-Inverse Document Frequency TF-IDF ; Bidirectional Encoder Representation from Transformers BERT ; Non-Negative Matrix Factorisation NMF ; cosine similarity; K-means clustering;

    Abstract : Help documents are important in an organization to use the technology applications licensed from a vendor. Customers and internal employees frequently use and interact with the help documents section to use the applications and know about the new features and developments in them. READ MORE

  6. 5. Developing Machine Learning-based Recommender System on Movie Genres Using KNN

    University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

    Author : Anthony Ezeh; [2023]
    Keywords : : Movie Recommender System; Machine Learning; Content-based Filtering; Collaborative Filtering; KNN Algorithms; Classification Algorithm;

    Abstract : With an overwhelming number of movies available globally, it can be a daunting task for users to find movies that cater to their individual preferences. The vast selection can often leave people feeling overwhelmed, making it challenging to pick a suitable movie. READ MORE