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Uporaba priporočilnih in odločitvenih sistemov za personalizacijo prodaje v mobilni aplikaciji
KOSEC, DAMJAN (Author), Rupnik, Rok (Mentor) More about this mentor... This link opens in a new window

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Abstract
Uporabniki se dandanes v nakupovalnem procesu soočajo z veliko količino informacij ter široko ponudbo produktov in storitev, kar kupcu onemogoča racionalno odločitev o nakupu tistih produktov in storitev, ki jih dejansko potrebuje v določenem času in kraju, in sicer glede na svoje želje, interese in potrebe. Z definiranjem in potrditvijo problema pri uporabnikih smo se lotili analize, načrtovanja, razvoja, testiranja in implementacije informacijskega in priporočilnega sistema za personalizacijo prodaje. Informacijski sistem deluje na poslovnem modelu, ki uporabniku za posredovanje pomembnih povratnih informacij v zameno ponudi akcijske ponudbe ali točke zvestobe. Pomanjkanje kvalitetnih informacij o kupcih, njihovih navadah, prihodnjih nakupih in preteklih izkušnjah je eden od ključnih razlogov, da podjetja ne morejo izvajati učinkovite personalizacije. Tako tudi v realnem času nimajo vseh odgovorov na pomembna vprašanja, ki se tičejo marketinga, prodaje in poslovanja podjetja. S pomočjo priporočilnih in odločitvenih sistemov in obdelave velike količine pametnih podatkov lahko kupcu ponudimo personalizirane produkte in storitve ter s tem pospešujemo in povečujemo prodajo, na drugi strani pa izboljšujemo uporabniško ter nakupovalno izkušnjo. Pri analizi in razvoju informacijskega in priporočilnega sistema smo postavili hipotezo, da bomo lahko z uporabo kvalitetnih podatkov o uporabnikovih željah, potrebah, preteklih izkušnjah in prihodnjih nakupih uporabniku ponudili veliko bolj personalizirane akcijske ponudbe. S personalizacijo bomo zelo povečali konverzijo CTR (angl. click to rate) med ogledi akcijskih ponudb in odgovori oziroma izvedbo prodajnih akcij. Na podlagi izdelanega in preizkušenega priporočilnega sistema sklepamo, da je za našo rešitev najprimernejša uporaba hibridnih priporočilnih tehnik, kjer se glede na različne situacije uporabita metodi filtriranja CF ali CB v kombinaciji z ostalimi odločitvenimi pravili in pogoji.

Language:Slovenian
Keywords:priporočilni sistemi, skupinsko filtriranje CF, vsebinsko filtriranje CB, Big Data, Internet stvari IoT, personalizacija
Work type:Master's thesis (m2)
Organization:FRI - Faculty of computer and information science
Year:2016
Views:572
Downloads:265
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Secondary language

Language:English
Title:The use of recommender and decision support systems for sales personalization in a mobile application
Abstract:
In the process of shopping, users are today faced with a large volume of information and a broad range of products and services that prevent them from being able to make rational decisions regarding the purchase of those products and services they actually require at a particular time and place and which meet their preferences, interests and needs. By defining and confirming this problem faced by users, we began with the analysis, design, development, testing and implementation of an information and recommendation system for the personalization of sales. This information system operates on the basis of a business model, where in exchange for providing important feedback, the user receives special offers or loyalty points. A lack of qualitative data about customers, their habits, future purchases and past experiences is one of the key factors in preventing companies from implementing effective personalization. Thus, even in real time, companies lack answers to important questions that concern marketing, sales and business operations. With the assistance of recommendation and decision making systems and by processing large amounts of smart data, we can offer the customer personalized products and services and thereby accelerate and increase sales volume while simultaneously improving the user and shopping experience. In the analysis and development of the information and recommendation system, we developed a hypothesis which proposed that with the use of qualitative data on user desires, needs, past experiences and future purchases, we could offer the user more personalized special offers. Personalization will also enable an increase of the CTR (Click to Rate) conversion between views of special offers and relevant responses, or rather, the execution of sales campaigns. On the basis of the developed and tested recommendation system, we conclude that the most appropriate solution for our purposes is the use of hybrid recommendation techniques which, depending on different types of situations, implement either the CF or CB method of filtering in combination with other decision rules and conditions.

Keywords:recommendation systems, CF collaborative filtering, CB content filtering, Big Data, Internet of Things IoT, personalization

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