Sökning: "Implicit interaction"

Visar resultat 1 - 5 av 66 uppsatser innehållade orden Implicit interaction.

  1. 1. EN STUDIE I MUNTLIG INTERAKTION - Spanska steg 5 och engelska 5 på lika villkor?

    Master-uppsats, Göteborgs universitet/Institutionen för didaktik och pedagogisk profession

    Författare :Ana Álvarez Björk; [2023-09-28]
    Nyckelord :second language learning; explicit and implicit language learning; pair speaking test; GERS; Extramural English; Spanish as L2 in Sweden; English as L2 in Sweden;

    Sammanfattning : Aim: The aims in this essay are to provide a description of interactional competence in a pair speaking test in Spanish and English, level B1.2 in a Swedish context. The investigated interactions are made by 14 pairs of students in upper secondary school studying the subjects English 5 and Spanish 5. LÄS MER

  3. 2. Randomized Diagonal Estimation

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Niclas Joshua Popp; [2023]
    Nyckelord :Diagonal estimation; randomized numerical linear algebra; low-rank approximation; matrix functions; Diagonalestimering; randomiserad numerisk linjär algebra; lågrankad approximation; matrisfunktioner;

    Sammanfattning : Implicit diagonal estimation is a long-standing problem that is concerned with approximating the diagonal of a matrix that can only be accessed through matrix-vector products. It is of interest in various fields of application, such as network science, material science and machine learning. LÄS MER

  4. 3. 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

  5. 4. Begränsad och befriad : En intervjustudie om unga kvinnors erfarenheter av kvinnliga könsroller i pingströrelsen

    Uppsats för yrkesexamina på avancerad nivå, Jönköping University/Högskolan för lärande och kommunikation

    Författare :Ebba Waldemarsson; [2023]
    Nyckelord :gender; feminine gender role; the Pentecostal movement; genus; kvinnlig könsroll; pingströrelsen;

    Sammanfattning : How the church views the female sex is an ongoing discussion, something that is noticeable in, among other things, the Christian newspaper Dagen, where there has been a recent debate about female leadership and the different or equal characteristics of the sexes. Against the background of this debate, this study is directed toward female gender roles in the Pentecostal movement. LÄS MER

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

    Master-uppsats, Stockholms universitet/Statistiska institutionen

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

    Sammanfattning : 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. LÄS MER