Recommender Systems in Light of Big Data

The growth in the usage of the web, especially e-commerce website, has led to the development of recommender system (RS) which aims in personalizing the web content for each user and reducing the cognitive load of information on the user. However, as the world enters Big Data era and lives through the contemporary data explosion, the main goal of a RS becomes to provide millions of high quality recommendations in few seconds for the increasing number of users and items. One of the successful techniques of RSs is collaborative filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. Despite its success, CF is facing some challenges posed by Big Data, such as: scalability, sparsity and cold start. As a consequence, new approaches of CF that overcome the existing problems have been studied such as Singular value decomposition (SVD). This paper surveys the literature of RSs and reviews the current state of RSs with the main concerns surrounding them due to Big Data. Furthermore, it investigates thoroughly SVD, one of the promising approaches expected to perform well in tackling Big Data challenges, and provides an implementation to it using some of the successful Big Data tools (i.e. Apache Hadoop and Spark). This implementation is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The implementation has been evaluated empirically by measuring mean absolute error which gave comparable results with other experiments conducted, previously by other researchers, on a relatively smaller data set and non-distributed environment. This proved the scalability of SVD-based RS and its applicability to Big Data

Analysis Of Properties Of Fatigue Loaded Backbone Discs

This study aims to find the effects of fatigue loading and rest on dynamic properties of healthy vertebra discs. The author’s objective is to use signal processing methods to analyze vertebra disc data and produce meaningful results of that analysis

Kolaborativno filtriranje

U ovom članku opisan je jedan tip sustava za preporuke, točnije kolaborativno filtriranje bazirano na modelu orijentiranom prema proizvodu i pripadni algoritam baziran na SVD dekompoziciji matrice sustava. Ukratko je dan kratak povijesni pregled sustava za preporuke te su navedeni osnovni pojmovi i opisani su najbitniji tipovi sustava za preporuke. Zatim je iskazan centralni algoritam rada, algoritam za preporuke zasnovan na SVD dekompoziciji s redukcijom dimenzije te je obrazložena njegova korektnost. Kroz svojstva navedenog algoritma naglašene su neke korisnosti SVD dekompozicije matrice koja ima mnoge primjene u stvarnom svijetu. Nakon toga opisana su dva načina za osvježavanje sustava novim korisnicima, folding-in metoda i update metoda te su dani pripadni algoritmi. U zadnjem dijelu rada opisani su testovi provedeni na implementiranim algoritmima, algoritmu za preporuke zasnovanom na SVD dekompoziciji s redukcijom dimenzije i folding-in metodi

Analysis Of Properties Of Fatigue Loaded Backbone Discs

This study aims to find the effects of fatigue loading and rest on dynamic properties of healthy vertebra discs. The author’s objective is to use signal processing methods to analyze vertebra disc data and produce meaningful results of that analysis

Collaborative Filtering

U radu je opisan jedan tip sustava za preporuke, točnije kolaborativno filtriranje bazirano na modelu orijentiranom prema proizvodu i pripadni algoritam baziran na SVD dekompoziciji matrice sustava. Ukratko je dan kratak povijesni pregled sustava za preporuke te su navedeni osnovni pojmovi i opisani su najbitniji tipovi sustava za preporuke. Zatim je iskazan centralni algoritam rada, algoritam za preporuke zasnovan na SVD dekompoziciji s redukcijom dimenzije te je obrazložena njegova korektnost. Kroz svojstva navedenog algoritma naglašena je korisnost SVD dekompozicije matrica u stvarnom svijetu. Nakon toga opisana su dva načina za osvje- žavanje sustava novim korisnicima, folding-in metoda i update metoda te su dani pripadni algoritmi. U zadnjem dijelu rada opisani su testovi provedeni na implementiranim algoritmima, algoritmu za preporuke zasnovanom na SVD dekompoziciji s redukcijom dimenzije i folding-in metodi, a pripadni Matlab kodovi priloženi su u Prilogu 2 i 3.The thesis is dealing with a special type of recommender system, with the main focus on the item and model based collaborative filtering recommender system including corresponding SVD based algorithm. The historical overview of recommender systems is briefly illustrated, key terms are listed and the most important types of recommender systems are described. Afterwards, the central algorithm of the thesis, the SVD algorithm for recommender system with dimensionality reduction, has been stated and its utility is motivated. The benefit of SVD decomposition in the real world has been outlined while describing properties of the central algorithm. After that, the folding-in method and the update method for updating system with new users have been described and related algorithms are given. The last section of the thesis displays the test examples of implemented algorithms, the central algorithm of the thesis and the folding-in method. Code implementation using Matlab is appended at the end of the thesis in Appendix 2 and Appendix 3