Παράλληλη και κατανεμημένη υλοποίηση αλγορίθμων συστάσεων (Bachelor thesis)
Μοσχόπουλος, Βασίλης
Recommendation systems are widely used today by a range of platforms for
product and service recommendations to consumers, customers and subscribers.
Recommendation systems’ goals include increasing sales, understanding the
market, as well as the consumers, and increasing the experience quality of platforms
and customer satisfaction.
Recommendation systems have come to play an important role in an era of
product and service abundance, with the goal of filtering products and services and
increasing recommendation quality to consumers, depending on their interests.
Notable recommendation systems can be found in platforms such as Google’s
YouTube, large e-shops such as Amazon and E-Bay, social platforms in the likes of
Facebook and Instagram and music platforms, for example, Spotify and
Soundcloud.
Meanwhile, libraries like numpy, pandas and scikit-learn can be used to
implement recommendation algorithms in software. In this thesis we study the
machine-learning library, PyTorch. PyTorch is a new addition to the deep learning
framework.
At a time when data become increasingly larger, usage of computer clusters for
the parallel distributed processing of data becomes a growing common trend.
In this thesis we analytically present and implement recommendation system
algorithms, specifically the collaborative filtering kind, with the use of PyTorch. We
study and train models of these algorithms, in a simple manner, as well as in a
parallel and parallel distributed way, with the use of processes, and conclude by
evaluating these algorithms in terms of accuracy and correctness.
Institution and School/Department of submitter: | Σχολή Τεχνολογικών Εφαρμογών/ Μηχανικών Πληροφορικής |
Subject classification: | Συστήματα συστάσεων (Φιλτράρισμα πληροφοριών) Recommender systems (Information filtering) Αλγόριθμοι Algorithms |
Keywords: | Μέθοδοι γειτονιών;Neighborhood based;Μονάδες μέτρησης ακρίβειας;Units of measurement;Συστήματα συστάσεων;Recommender systems;Αλγόριθμοι;Algorithms |
Description: | Πτυχιακή εργασία - Σχολή Τεχνολογικών Εφαρμογών - Τμήμα Μηχανικών Πληροφορικής, 2019 (α/α 11293) |
URI: | http://195.251.240.227/jspui/handle/123456789/14199 |
Appears in Collections: | Πτυχιακές Εργασίες |
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File | Description | Size | Format | |
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Mosxopoulos.pdf | 3.96 MB | Adobe PDF | View/Open |
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