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master's thesis
Stvarno-vremenske preporuke zasnovane na povjerenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. urn:nbn:hr:168:488058
Đuričić, Tomislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Đuričić, T. (2015). Stvarno-vremenske preporuke zasnovane na povjerenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:488058

Đuričić, Tomislav. "Stvarno-vremenske preporuke zasnovane na povjerenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:488058

Đuričić, Tomislav. "Stvarno-vremenske preporuke zasnovane na povjerenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:488058

Đuričić, T. (2015). 'Stvarno-vremenske preporuke zasnovane na povjerenju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:488058

Đuričić T. Stvarno-vremenske preporuke zasnovane na povjerenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2015 [cited 2024 June 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:488058

T. Đuričić, "Stvarno-vremenske preporuke zasnovane na povjerenju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:168:488058

Metadata
TitleStvarno-vremenske preporuke zasnovane na povjerenju
Title (english)Real-time recommendations based on social trust
AuthorTomislav Đuričić
MentorVedran Podobnik (mentor)
Committee memberVedran Podobnik (predsjednik povjerenstva)
Committee memberMarina Bagić Babac (član povjerenstva)
Committee memberIgnac Lovrek (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2015-09-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Istraživanja u području preporučiteljskih sustava su vrlo popularna u posljednjih nekoliko godina. Iako danas postoje razni pristupi za preporučivanje, još uvijek ima mjesta za napredak na području stvarno-vremenskih preporuka koristeći velike skupove podataka u kojima su podaci raspršeni. Kako bi se nosilo sa raspršenim podacima, ovaj rad analizira različite pristupe preporučivanja zasnovane na povjerenju koji poboljšavaju točnost standardno korištenih pristupa koje koriste Kolaborativno Filtriranje. Kako bi se pokazalo da se pristupi za preporučivanje zasnovani na povjerenju mogu primjeniti za kreiranje stvarno-vremenskih preporuka, u sklopu ovog rada je nadograđen ScaR, skalabilni preporučiteljski sustav. ScaR je nadograđen sa pristupima preporučivanja koji računaju povjerenje među korisnicima koristeći Apache Solr mehanizam za pretraživanje. Rezultati provedenih eksperimenata su pokazali da se koristeći pristupe preporučivanja zasnovane na povjerenju mogu dostaviti kvalitetne preporuke u stvarnom vremenu.
Abstract (english)
Research on recommender systems has gained a tremendous popularity in recent years. Although various recommender approaches are available nowadays, there is still a lack of work that tackles real-time recommendation on large and sparse data. To tackle the data sparsity problem, this thesis analyzes different trust-based approaches which improve the accuracy of the usually used Collaborative Filtering recommendation approaches. To show how the trust-based approaches can also be applied to generate real-time recommendations, this thesis extended ScaR, a scalable recommendation framework, with recommendation approaches which calculate the trust values between users using the Apache Solr search engine. Experimental results showed that using trust-based approaches, high quality recommendations can be served in realtime.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:488058
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2018-02-06 08:39:47