Εφαρμογές τεχνικών μηχανικής μάθησης σε δεδομένα γράφων
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Keywords
Δεδομένα γράφων ; Μηχανική μάθηση ; Συστήματα συστάσεων ; Νευρωνικά δίκτυα ; ΠάιθονAbstract
In our modern age recommender systems rely heavily on machine learning to
automatically learn and improve over time based on user data and feedback. Many
websites and applications such as social networks, content services, e-commerce
platforms and others use recommendation algorithms to help users find new goods,
services or information that may be of interest to them. In order to model a user's tastes
and interests, recommender systems use machine learning algorithms to evaluate vast
amounts of user data, including ratings, search queries and past purchases. Then,
using this model, personalized recommendations are generated based on each user's
requirements and tastes.
In this master's thesis we aim to study the theory of graphs, meaning the types of
graphs, the way they are visualized, applied and the problems during their application.
Also we examine in machine learning its forms, its models, its applications and its
limitations. Afterwards we proceeded to a practical experiment. For our experiment our
methodology was to take the MovieLens 100K dataset, explore it by making useful
graph data analysis inferences, bring it into the appropriate heterogeneous bidirectional
graph format which we eventually used to train a graph neural network algorithm
recommendation system. Finally we did two experiments changing the training criteria
and measured the performance of the algorithm in each case with the metrics binary
cross entropy loss with logits (loss) and area under the ROC curve (AUC) which we
visualized in graphs for each case to make it easier to see the results. In the end we
concluded in both cases that our system performed "outstanding".