Essays about: "Implicit ratings"

Showing result 1 - 5 of 9 essays containing the words Implicit ratings.

  1. 1. 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

  3. 2. Overcoming The New Item Problem In Recommender Systems : A Method For Predicting User Preferences Of New Items

    University essay from Stockholms universitet/Statistiska institutionen

    Author : Alice Jonason; [2023]
    Keywords : Recommender systems; Content-based systems; Implicit ratings; Latent Dirichlet Allocation; Vector Space Model;

    Abstract : This thesis addresses the new item problem in recommender systems, which pertains to the challenges of providing personalized recommendations for items which have limited user interaction history. The study proposes and evaluates a method for generating personalized recommendations for movies, shows, and series on one of Sweden’s largest streaming platforms. READ MORE

  4. 3. Contextualizing music recommendations : A collaborative filtering approach using matrix factorization and implicit ratings

    University essay from Linköpings universitet/Institutionen för datavetenskap

    Author : Alexander Häger; [2020]
    Keywords : recommender; recommender systems; context; context-aware; CARS; music; music streaming; matrix factorization;

    Abstract : Recommender systems are helpful tools employed abundantly in online applications to help users find what they want. This thesis re-purposes a collaborative filtering recommender built for incorporating social media (hash)tags to be used as a context-aware recommender, using time of day and activity as contextual factors. READ MORE

  5. 4. Leerec : A scalable product recommendation engine suitable for transaction data.

    University essay from Mittuniversitetet/Avdelningen för informationssystem och -teknologi

    Author : Anton Flodin; [2018]
    Keywords : Collaborative filtering; log processing; event; Alternating Least Square;

    Abstract : We are currently living in the Internet of Things (IoT) era, which involves devices that are connected to Internet and are communicating with each other. Each year, the number of devices increases rapidly, which result in rapid growth of data that is generated. READ MORE

  6. 5. Investigating the performance of matrix factorization techniques applied on purchase data for recommendation purposes

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

    Author : John Holländer; [2015]
    Keywords : Recommender systems; Matrix factorization; Machine learning; Implicit feedback; Recommender algorithms; Nearest neighbor; UserNN; Recommendation systems; UserKnn;

    Abstract : Automated systems for producing product recommendations to users is a relatively new area within the field of machine learning. Matrix factorization techniques have been studied to a large extent on data consisting of explicit feedback such as ratings, but to a lesser extent on implicit feedback data consisting of for example purchases. READ MORE