Essays about: "movielens"
Showing result 1 - 5 of 15 essays containing the word movielens.
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1. Federated Neural Collaborative Filtering for privacy-preserving recommender systems
University essay from Uppsala universitet/Avdelningen för systemteknikAbstract : In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. READ MORE
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2. Hellinger Distance-based Similarity Measures for Recommender Systems
University essay from Umeå universitet/StatistikAbstract : Recommender systems are used in online sales and e-commerce for recommending potential items/products for customers to buy based on their previous buying preferences and related behaviours. Collaborative filtering is a popular computational technique that has been used worldwide for such personalized recommendations. READ MORE
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3. Comparison and improvement of time aware collaborative filtering techniques : Recommender systems
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : Recommender systems emerged in the mid '90s with the objective of helping users select items or products most suited for them. Whether it is Facebook recommending people you might know, Spotify recommending songs you might like or Youtube recommending videos you might want to watch, recommender systems can now be found in every corner of the internet. READ MORE
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4. Comparing neighborhood and matrix factorization models in recommendation systems : Saving the user some clicks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This thesis explores how different recommendation models based on machine learning can be implemented using customer activity data from an internet portal. The recommendation models have been evaluated on both error metrics and accuracy metrics. READ MORE
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5. Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?
University essay from Uppsala universitet/Statistiska institutionenAbstract : One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. READ MORE
