Customer perspective to sharing location based data

Location Based Service (LBS) has the potential to be one of the most influential aspects in the digital business world. LBS opens a large amount of opportunities to the business world and gives access to customers directly in real time. LBS is capable of creating customer value by delivering context-relevant messages directly to customers based on their current location, activities, interests, and preferences. Additionally, in order for the LBS to function properly and bring the expected outcomes, it is vital to have the essential technological solution, as well as to understand customers’ perspectives of sharing location based data (LBD). Although, remarkable progress has been made in LBS technology on the research and development side, customers’ perspectives of LBD is largely unexplored, especially in academia. Therefore, the purpose of this study is to build a customer perspective to sharing LBD. In order to do that, customer value has been chosen as the key theoretical concept. Customer value is widely used in identifying customers’ perceived benefits and sacrifices. The study has been conducted by taking an interpretive approach based on qualitative data, collected through focus group discussion and face-to-face interview. The results indicated that people’s willingness to share location data varies on several characteristics. Consumer identified navigation, exploring a new place, getting discounts and being part of the society are some of the fundamental perceived benefits of sharing LBD. On the other hand, sharing LBD comes with certain risks, as the data revealed consumer concern over risks involving privacy, physical risks, monetary risks, and risks of intrusion

Mixture Models for Multidimensional Positive Data Clustering with Applications to Image Categorization and Retrieval

Model-based approaches have become important tools to model data and infer knowledge. Such approaches are often used for clustering and object recognition which are crucial steps in many applications, including but not limited to, recommendation systems, search engines, cyber security, surveillance and object tracking. Many of these applications have the urgent need to reduce the semantic gap of data representation between the system level and the human being understandable level. Indeed, the low level features extracted to represent a given object can be confusing to machines which cannot differentiate between very similar objects trivially distinguishable by human beings (e.g. apple vs tomato). Such a semantic gap between the system and the user perception for data, makes the modeling process hard to be designed basing on the features space only. Moreover those models should be flexible and updatable when new data are introduced to the system. Thus, apart from estimating the model parameters, the system should be somehow informed how new data should be perceived according to some criteria in order to establish model updates. In this thesis we propose a methodology for data representation using a hierarchical mixture model basing on the inverted Dirichlet and the generalized inverted Dirichlet distributions. The proposed approach allows to model a given object class by a set of components deduced by the system and grouped according to labeled training data representing the human level semantic. We propose an update strategy to the system components that takes into account adjustable metrics representing users perception. We also consider the "page zero" problem in image retrieval systems when a given user does not possess adequate tools and semantics to express what he/she is looking for, while he/she can visually identify it. We propose a statistical framework that enables users to start a search process and interact with the system in order to find their target "mental image". Finally we propose to improve our models by using a variational Bayesian inference to learn generalized inverted Dirichlet mixtures with features selection. The merit of our approaches is evaluated using extensive simulations and real life applications