<p>With the aim to investigate existence of difference between responses of selected groups of young consumers representatives toward information associated with knowledge about food quality, labeling, safety and conditions of the product use, the results of survey were analyzed crossing the groups of consumers regarding: (1) education and (2) gender, with the other variables: (i) information related to food quality, labeling and food safety; (ii) information related to food safety; (iii) information associated with individual experience in food purchasing, preparing and consuming. The questionnaire offered answers with three grade of importance. Our research showed that groups of students formed on the basis of their education and gender, in our survey considered as representatives of young adults, had different interest for selected set of information included in the statements connected with food quality, safety and food choice. The results showed that there is a need for better informing and education of consumers about food quality and safety, labels and labeling, and how to use and interpret labels content. The results of research represent a qualitative set of information related to the food preference, which could be useful for creation and development guidelines for consumers’ better informing and education.</p>
Text mining to a great extent depends on the various text preprocessing techniques. The preprocessing methods and tools which are used to prepare texts for further mining can be divided into those which are and those which are not language-dependent. The subject matter of this research was the analysis of the influence of these methods and tools on further text mining. We first focused on the analysis of the influence on the reduction of the vector space model for the multidimensional represen-tation of text documents. We then analyzed the influence on calculating text similarity, which is the focus of this research. The conclusion we reached is that the implemen-tation of various text preprocessing methods in the Serbian language, which are used for the reduction of the vector space model for the multidimensional representation of text document, achieves the required results. But, the implementation of various text preprocessing methods specific to the Serbian language for the purpose of calculating text similarity can lead to great differences in the results.
In this paper we applied twinning algorithm for product that are sold via e-commerce platform. To establish relatively homogenous product groups that were on sale on this e-commerce platform during the last year, it was necessary to form predictive mathematical model. We determined set of relevant variables that will represent group attributes, and we applied K-means algorithm, Market Basket model and Vector Distance model. Based on analysis of basic and derived variables, fixed number of clusters was introduced. Silhouette index was used for the purposes of detecting whether these clusters are compact. Using these cluster separations, we created models that detect similar products, and try to analyze probability of sales for each product. Analysis results can be used for planning future sales campaigns, marketing expenses optimization, creation of new loyalty programs, and better understanding customer behavior in general.
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