Using augmented reality and location-awareness to enhance visitor experience: A case study of a theme park app

Mobile travel has become a significant trend due to the emergence of smartphones and mobile technology. Existing researches show that the integration of location-based services (LBS) is potentially adding significant values to the mobile tourism industry. The purpose of this study is to develop a location-based mobile application for theme park visitors to enhance their visiting experience. The application of location based solutions such as interactive map, push notification services with geo fencing and augmented reality tour guide will be involved in this study. Google Map SDK for Android, Google ARCore Sceneform, GeoFire and SQLite methodology is adopted to build the application. An online survey is conducted to evaluate the user acceptance for the mobile application by sending Android Package Kit (APK) to the users. The result shows that majority of respondents had positive attitude towards the application and agreed that the application will enhance their park visiting experience. The conclusion can be drawn that the application can be further improved with features which contributes to the digitalization of outdoor parks or other fields in mobile tourism

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Leveraging Mobile App Classification and User Context Information for Improving Recommendation Systems

Mobile apps play a significant role in current online environments where there is an overwhelming supply of information. Although mobile apps are part of our daily routine, searching and finding mobile apps is becoming a nontrivial task due to the current volume, velocity and variety of information. Therefore, app recommender systems provide users’ desired apps based on their preferences. However, current recommender systems and their underlying techniques are limited in effectively leveraging app classification schemes and context information. In this thesis, I attempt to address this gap by proposing a text analytics framework for mobile app recommendation by leveraging an app classification scheme that incorporates the needs of users as well as the complexity of the user-item-context information in mobile app usage pattern. In this recommendation framework, I adopt and empirically test an app classification scheme based on textual information about mobile apps using data from Google Play store. In addition, I demonstrate how context information such as user social media status can be matched with app classification categories using tree-based and rule-based prediction algorithms. Methodology wise, my research attempts to show the feasibility of textual data analysis in profiling apps based on app descriptions and other structured attributes, as well as explore mechanisms for matching user preferences and context information with app usage categories. Practically, the proposed text analytics framework can allow app developers reach a wider usage base through better understanding of user motivation and context information