Recommender systems comparison
Σύγκριση συστημάτων ανάλυσης δεδομένων για παραγωγή προτάσεων
View/
Keywords
Συστήματα προτάσεων ; Recomender Systems ; Μηχανική μάθηση ; Machine learningAbstract
The last decade Internet has been flooded with information. Information
that no one can filter to find what he needs, raw data, videos, music or
products. Large retail sites like Amazon developed recommenders systems
in order to offer products to their users. The need although is not limited
only to the retail area.
Web sites like Youtube or Vimeo need to recommend to each user of their,
videos that may like to watch next. Facebook is another example of an
application utilizing lots of data and offering recommendations on what you
may want to read or who may be a friend of yours. Most of the times, a
recommender system is not the core functionality of an application. It is
through a very useful feature that gives a clear advantage in any business
area needed.
This thesis aims to distinguish metrics on recommender systems that can
be proved useful to compare them. Also, this thesis performs a comparison
between two algorithms of the collaborative filtering family. The content-based with focus on items and the machine learning oriented alternating
least square (als).
Postgraduate Studies Programme
Ψηφιακά Συστήματα και ΥπηρεσίεςDepartment
Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών ΣυστημάτωνNumber of pages
71Language
EnglishCollections
Except where otherwise noted, this item's license is described as
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές