Towards a social and context-aware mobile recommendation system for tourism

[EN] Loyalty in tourism is one of the main concerns for tourist organizations and researchers alike. Recently, technology in general and CRM and social networks in particular have been identified as important enablers for loyalty in tourism. This paper presents POST-VIA 360, a platform devoted to support the whole life-cycle of tourism loyalty after the first visit. The system is designed to collect data from the initial visit by means of pervasive approaches. Once data is analysed, POST-VIA 360 produces accurate after visit data and, once returned, is able to offer relevant recommendations based on positioning and bio-inspired recommender systems. To validate the system, a case study comparing recommendations from the POST-VIA 360 and a group of experts was conducted. Results show that the accuracy of system’s recommendations is remarkable compared to previous efforts in the field

An Integrative Model of Managing Software Security during Information Systems Development

This study investigates the critical relationship between organizational system development policies, procedures and processes and the resulting security quality of the systems developed. We draw from a general software quality model to provide a theoretical foundation for testing this relationship. We used paper-based survey as well as online surveys to collect data from software developers and project managers. Our results revealed a significant relationship between management support and security policies and development process control. We also found significant relationships between development-process control and security quality, attitude and security quality, and the interaction between value congruence and commitment to provide security skills development. Counter-intuitively, we did not find a significant relationship between either security policy and security quality or the interaction between security policy and its legitimacy as perceived by systems development personnel. The managerial implications of the study include the need to foster a climate of security skills development through training for system development personnel and also simultaneously find strategies to more closely align their values to the security goals of the organization. Additionally, providing management support to formulate guidelines for development process control can improve the security quality of the systems developed

Personalized program guides for digital television.

Razvoj digitalne televizije je doveo do značajnog porasta broja TV sadržaja dostupnih korisnicima, ali je otežao izbor onog koji je od interesa. Sve do pojave personalizovanih programskih vodiča sposobnih da nauče korisnička interesovanja i preporuče odgovarajuće sadržaje nije postojalo rešenje koje je na adekvatan način razmatralo ovaj problem. Ranija rešenja, kao što su štampani i elektronski vodiči, su pretežno samo pretvarala problem viška informacija u drugi oblik. Napredak tehnologije i društva postavlja sve veće zahteve pred personalizovane programske vodiče za digitalnu televiziju, što zahteva njihovo pažljivo planiranje i projektovanje. Vodiči moraju da budu u mogućnosti da modeliraju različite načine donošenja odluka pojedinačnih korisnika, da rade u realnom vremenu na mobilnim uređajima s ograničenim hardverskim resursima, da vode računa o karakteristikama prikupljenih podataka, da uzimaju u obzir kontekst u kome se pristupa TV sadržaju i da štite privatnost svih korisnika, jer neki od njih nisu svesni mogućih opasnosti. Pažljivim izborom arhitekture i algoritma učenja, lokalno implementiran vodič baziran na neuralnim mrežama može da ispuni sve ove zahteve. S obzirom na to da korisnici znatno češće pružaju informacije o sadržajima koji im se dopadaju nego o onim koji im se ne dopadaju, u ekstremnim slučajevima se dešava to da su prikupljene samo pozitivne interakcije. Da bi se taj problem prevazišao, predložen je sistem s dva režima rada. U prvom režimu sistem uči i pruža preporuke samo na osnovu TV sadržaja koje korisnik voli, dok u drugom izjednačava uticaj sadržaja koje korisnik voli i onih koje ne voli na proces pružanja preporuka. Povećan uticaj pozitivnih interakcija dovodi do degradacije predikcije sadržaja koje posmatrač ne želi da gleda, te će se, usled greške u klasifikaciji, neželjeni sadržaji često pojavljivati u listi preporuka i na taj način smanjiti zadovoljstvo korisnika. Korišćenjem serije simulacija pokazali smo da je postignuto trajanje treniranja neuralne mreže kratko, čak i na uređajima s ograničenim hardverskim resursima. Zaključak je da je predloženi vodič veoma pogodan za implementaciju na mobilnim uređajima od kojih se očekuje da u budućnosti postanu dominantan način pristupa TV sadržajima.The development of digital television significantly increased the quantity of media contents available to the users, but made it difficult to make the right choice. Before the invention of the personalized program guides capable of learning user preferences and recommending adequate contents, there were no means of properly addressing this problem. Former solutions, such as printed or electronic program guides, mostly converted the problem of having to deal with too much information into another form. The advancements in both technology and society put higher demands to the personalized program guides for digital TV, which require careful planning and design processes. Guides must be able to model various individual decision making approaches, work in real-time on mobile devices with limited hardware resources, take into account the characteristics of the collected data, take into consideration the program accessing context and protect the privacy of all users, since some of them are not aware of the possible risks. By carefully choosing the architecture and learning algorithms, a locally implemented guide based on neural networks can fulfil all the aforementioned requirements. Due to the fact that the users provide information about the content they like much more often than about the one they dislike, only positive interactions are collected in extreme cases. In order to overcome that situation, a system having two operating modes is proposed. The first mode enables the system to learn and give recommendations based on preferred TV contents, while the second equalizes the influence of the liked and disliked contents on the recommending process. The increased influence of positive interactions degrades the unwanted content prediction process, resulting in classification error, appearance of unwanted content in the recommendation list and user dissatisfaction. By applying a series of simulations, we showed the accomplished neural network training time to be short, even in cases of devices with limited hardware resources. It can be concluded that the proposed guide is very convenient for implementation on mobile devices which are expected to become a dominant way of accessing media contents in the future

The use of recommender and decision support systems for sales personalization in a mobile application

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

The use of recommender and decision support systems for sales personalization in a mobile application

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