Personality Based Recommendation System Using Social Media

Recommendation system is the reason of success for most of the social media companies as well as e-commerce sites. Giving recommendation to the uses is one of the interesting and challenging tasks nowadays, it helps to generate revenue, to increase number of users, to reduce the searching time for particular item. Recommendation system helps for making interest in user and eventually it increases the popularity of any site. Huge number of items (product, users, movies, songs, hotels etc.) and its feature sets makes it hard to predict the accurate items to the user. It is important to keep all historic data of user as well as all information about the items to generate recommendation. In this paper, the personality of the user is used with the combination on the most popular recommendation techniques like collaborative filtering (CF) and content based filtering (CB) proposed on the amazon review data set. In the first model the personality of the user is calculated by using the big five model on the twitter account. In the second module Collaborative filtering is used to generate the recommendation based on the historic information of the user wherries in third module, Content based filtering is uses to generate recommendation based on the feature set of the item. Pearson-correlation algorithm is applied on both modules and ranking are generated. Finally union of the both vector space are taken as the final recommendation

SURVEILLANCE SYSTEM BASED ON QUADRANTS AS SUPPORT IN URBAN POPULATIONS

El propósito de este trabajo de investigación es el desarrollo de un sistema con capacidad de procesar los datos obtenidos en tiempo real por parte de ciudadanos, los cuales ofrecen control de riesgo y de protección de privacidad y seguridad, para las comunidades urbanas. Para tal propósito, se diseñó una arquitectura capaz de gestionar las peticiones de los ciudadanos y generar las respuestas por parte del cuadrante más cercano a la solicitud. Además, el sistema tiene la capacidad de generar recomendaciones asociadas a los sitios que generen riesgo a las personas que lo visitan. Los resultados de las pruebas del sistema demostraron una mejora significativamente en los tiempos de respuesta de la Policía frente a las solicitudes de los ciudadanos. Este trabajo de investigación se desarrolló bajo los parámetros del Plan Nacional de Vigilancia Comunitaria por Cuadrantes (PNVCC) de la Policía Nacional de Colombia.PALABRAS CLAVES: Computación urbana, computación ubicua, GPS, QR-Code, sistemas de recomendación, teoría de colas.The purpose of this research is to develop a system capable of processing the data in real time by citizens, which offer risk control and privacy protection and security for urban communities. For this purpose architecture capable of managing the requests of citizens and generate responses from the nearest quadrant design application. The system also has the ability to generate recommendations associated with sites that create risk to people who visit. The results of the system tests showed significantly improved response times of the Police against citizen’s requests. This research was developed under the parameters of the National Plan for Community Surveillance by Quadrants (PNVCC) of the National Police of Colombia.KEYWORDS: Urban Computing, Urban Security, ubiquitous computing, GPS, QR-Code, recommender system, queuing theory