Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu

Mihaljević, Branko

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scientific master's theses

Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2005. urn:nbn:hr:168:001356

Mihaljević, Branko

University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Control and Computer Engineering

Cite this document

APA 6th Edition

Mihaljević, B. (2005). Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu (Scientific master's theses). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:001356

MLA 8th Edition

Mihaljević, Branko. "Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu." Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, 2005. https://urn.nsk.hr/urn:nbn:hr:168:001356

Chicago 17th Edition

Harvard

Mihaljević, B. (2005). 'Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu', Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:001356

Vancouver

Mihaljević B. Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu [Scientific master's theses]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2005 [cited 2025 January 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:001356

IEEE

B. Mihaljević, "Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu", Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2005. Available at: https://urn.nsk.hr/urn:nbn:hr:168:001356

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:001356

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Metadata

Title	Model preporučiteljskog sustava temeljen na umjetnom imunološkom sustavu
Title (english)	Recommender system model based on artificial immune system
Author	Branko Mihaljević
Mentor	Mario Žagar (mentor)
Committee member	Mario Žagar (član povjerenstva)
Committee member	Mario Kovač (član povjerenstva)
Committee member	Davor Antonić (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing (Department of Control and Computer Engineering) Zagreb
Defense date and country	2005-12-21, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing Process Computing
Universal decimal classification (UDC)	004 - Computer science and technology. Computing. Data processing
Abstract	Zbog svojih brojnih svojstava i principa funkcioniranja umjetni imunološki sustavi su primjenjivi u domeni problema klasifikacije i ocjenjivanja objekata s uočljivim značajkama. Detaljnim uvidom u mogućnosti primjene umjetnih imunoloških sustava zaključuje se da su pogodni za rješavanje problema preporuke zanimljivih sadržaja, poštujući evoluciju korisničkih sklonosti i interesa. Jedna od novih teorija kontekstno zavisnih odgovora u području imunoloških sustava, nazvana teorija opasnosti, objašnjava reakcije imunoloških stanica na patogene. Ovaj rad opisuje izgradnju modela preporučiteljskog sustava za potrebe preporuke sadržaja temeljenog na umjetnom imunološkom sustava uz korištenje teorije opasnosti. Analizom potreba preporučiteljskog sustava definirani su zahtjevi i svojstva modela, te je predložena arhitektura sustava. Na osnovu usporedbe s drugim umjetnim imunološkim sustavima, predloženo je detaljno radno okruženje kroz reprezentaciju komponenata sustava, odabir više različitih mjera sličnosti za usporedbu vektora značajki, i odabir procedura upravljanja dinamikom i životnim ciklusom komponenata sustava. Za potrebe modela, projektiran i izgrađen je algoritam RECAIS, te je mjerena njegova učinkovitost s realnim podacima. Model sustava je primijenjen na problem preporuke vijesti FERWeb portala te je diskutirana mogućnost primjene preporuke sadržaja u drugim sustavima za upravljanje sadržajem.
Abstract (english)	Because of their characteristics and principles of functioning, artificial immune systems are applicable in classification and rating problem domain on objects with perceptible features. Detail inquiry of AIS application possibilities concludes suitability for solving content recommendation problems, honoring evolving user preferences and interests. One of novel context dependant response theories in area of immune systems, called Danger theory, explains immunological cells reactions to pathogens. This thesis addresses recommender system model construction for content recommendation based on artificial immune system with Danger theory. Recommender system requirement analysis defines model requisites and properties, and system architecture is being proposed. Based on comparison with other artificial immune systems, detail model framework is proposed via system component representation, selection of different similarity measures for feature vector comparison, and selection of procedures governing system dynamics and components lifecycle. Regarding model requirements, algorithm RECAIS is designed and implemented, and its efficiency is measured with real data. System model is applied to news recommendation problem on FERWeb portal. Content recommendation application capabilities with other content management systems are discussed.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:001356
Study programme	Title: Postgraduate master programe in computing Study programme type: university Study level: postgraduate Academic / professional title: magistar inženjer računarstva (magistar inženjer računarstva)
Type of resource	Text
File origin	Born digital
Access conditions	Closed access
Terms of use
Repository	FER Repository
Created on	2019-07-16 07:21:07