Alireza Kazeminia

Bio: Alireza Kazeminia is an academic researcher from University of Isfahan. The author has contributed to research in topics: Mobile database & Mobile computing. The author has an hindex of 2, co-authored 2 publications receiving 16 citations.

Personality-Based Personalization of Online Store Features Using Genetic Programming: Analysis and Experiment

Abstract: The decisions made by the customers in online environments are influenced by their personality characteristics. Each customer in an online environment relies more heavily on certain features of a store to make decisions while ignoring others. Thus, personalizing these features may streamline the decision-making process and increase satisfaction. In this paper, an intelligent method for personalizing the features of an online store according to the users’ personality is presented. In the proposed method, using genetic programming several equations are developed to estimate how users with different personality characteristics prefer various features of an online store. These equations are then used for personalization of the store features to increase customers’ satisfaction and persuade them to make larger purchases. The evaluation on a sample of 194 individuals indicates that the obtained equations are able to estimate the user’s preferences with over 80% accuracy in most cases. In addition, empirical assessment of the obtained equations shows that the proposed personalization method improves the user satisfaction.

A privacy protection mechanism for location-based mobile coupons

Abstract: Mobile technologies have created unprecedented opportunities for innovative marketing strategies. Location-based mobile coupons are at the cutting edge of these strategies. They provide a method to offer deals to mobile customers based on where they are at a certain time. However, revealing one's location in exchange for a service poses privacy threats ranging from discovery of personal details to being watched or tracked. This paper proposes a novel privacy protection mechanism for location-based coupons using an anonymous authentication method. The proposed mechanism protects against two adversaries: a global adversary that observes all the exchanged location information and an eavesdropper that listens to the communications on the wireless channel. It ensures user privacy and anonymity and prevents users from being tracked using self-generated pseudonyms. Furthermore, restriction applied on pseudonyms avoids all coupons to be grabbed by a greedy user.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The Path between Personality, Self-Efficacy, and Shopping Regarding Games Apps

Abstract: The smartphone has made gaming more accessible and desirable for a wider market than ever before. Game apps are one of the most consumed and fastest growing products in the world today. Yet, few studies have thus far explored the implications of games apps consumption from a consumer perspective, addressing the transfer of abilities from one technological field to another. The main purpose of this paper is threefold: to ascertain the role of personality as a determinant of self-efficacy, to establish whether there is a transfer process from self-efficacy in video gaming with apps to online shopping and to analyze the impact of self-efficacy on the online purchase of game-related products. Results show that neuroticism, extraversion, and agreeableness determine the gaming self-efficacy that is transferred to online shopping self-efficacy and finally to the online purchase of game-related products. These insights provide interesting managerial implications that could affect video game marketing.

Factors influencing occupational accidents: a multidimensional analysis in the electricity sector

Abstract: Occupational accidents are a public health problem; therefore, it is necessary to conduct research that contributes to accident prevention and health promotion. To this end, this study aimed to investigate the main factors influencing work accidents at an electric power company. A survey of 1,027 in-house and outsourced employees of a large electric utility was conducted. The participants included injured and non-injured professionals. Organizational, personal/behavioral, and work/task factors were found to have statistically significant effects on work accident occurrence. As an academic and managerial contribution, the identification of the main factors that influence work accidents in the electric sector favors the development of strategies and actions for its control and mitigation.