Recommender System Model Based on Artificial Immune System (CROSBI ID 520932)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Mihaljević, Branko ; Cvitaš, Ana ; Žagar, Mario
engleski
Recommender System Model Based on Artificial Immune System
Recommendation and prediction problems mostly rely on recognition and classification tasks. Artificial immune systems, based on natural immunological principles, are computational paradigm for solving such tasks. Additional context dependent response theories like Danger theory explain usage of signaling in recognition process. Recommender system model proposed in this paper addresses construction of a web portal news article recommender based on artificial immune system combined with Danger theory. System knowledge represents learned user preferences using implicit tracking of user actions. System also adapts to evolution of user's opinion and expresses results in personalized recommendation list format.
Artificial Immune System; Danger Theory; Recommender System; RECAIS
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Podaci o prilogu
367-372-x.
2006.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the 28th International Conference on Information Technology Interfaces - ITI 2006
Lužar-Stiffler, Vesna ; Hljuz Dobrić, Vesna
Zagreb: Sveučilišni računski centar Sveučilišta u Zagrebu (Srce)
Podaci o skupu
28th International Conference on Information Technology Interfaces - ITI 2006
predavanje
19.06.2006-22.06.2006
Cavtat, Hrvatska