http://hdl.handle.net/20.500.12128/21904
Tytuł: | Speed up Differential Evolution for ranking of items in recommendation systems |
Autor: | Boryczka, Urszula Bałchanowski, Michał |
Słowa kluczowe: | recommendation systems; ranking function; learning to rank; differential evolution; metaheuristic; profile injection attack |
Data wydania: | 2021 |
Źródło: | "Procedia Computer Science", Vol. 192, 2021, s. 2229-2238 |
Abstrakt: | of the generated recommendations, different techniques are often used which try to personalize recommendations. Usually user preferences are stored in the form of a vector in which individual values describe to what extent a given feature is desired by the user. To find this vector, metaheuristic algorithms can be used, however their main drawback is their computational complexity. Therefore, in this paper, a modification of the Differential Evolution algorithm is proposed to enable faster computation of the ranking score for each item in the system, which is used to create a recommendation list. Experiments have been performed on the current MovieLens 25m database and they show that our modification can significantly speed up the process of finding a preference vector, without losing their quality for the top-N recommendation task. We will also address the vulnerability of recommendation systems to profile injection attacks, as a result of which an attacker can influence the generated recommendations. |
URI: | http://hdl.handle.net/20.500.12128/21904 |
DOI: | 10.1016/j.procs.2021.08.236 |
ISSN: | 1877-0509 |
Pojawia się w kolekcji: | Artykuły (WNŚiT) |
Plik | Opis | Rozmiar | Format | |
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Boryczka_Balchanowski_Speed_up_Differential.pdf | 1,07 MB | Adobe PDF | Przejrzyj / Otwórz |
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