Being a popular social network type, online dating sites provide a platform for people to find partners for establishing a relationship. In this study, a recommendation engine for one of the prominent online dating sites of Turkey is developed. It works as a support system to suggest potential matches to the site users. As opposed to the traditional systems that match users based on their revealed preferences, the engine is based on a rule set extracted from the past communication data, using association rule mining. A list of best matches based on scoring derived from these rules is presented. The performance of the engine is statistically tested. It is found that the scores of matching couples are found to be significantly higher than the non-matched couples' scores.

Networks provide useful tools for analysing diverse complex systems from natural, social and technological domains. Growing size and variety of data such as more nodes and links and associated weights, directions and signs can provide accessory information. Link and weight abundance, on the other hand, results in denser networks with noisy, insignificant or otherwise redundant data. Moreover, typical network analysis and visualization techniques presuppose sparsity and are not appropriate or scalable for dense and weighted networks. As a remedy, network backbone extraction methods aim to retain only the important links while preserving the useful and elucidative structure of the original networks for further analyses. Here, we provide the first methods for extracting signed network backbones from intrinsically dense unsigned unipartite weighted networks. Utilizing a null model based on statistical techniques, the proposed significance filter and vigor filter allow inferring edge signs. Empirical analysis on migration, voting, temporal interaction and species similarity networks reveals that the proposed filters extract meaningful and sparse signed backbones while preserving the multiscale nature of the network. The resulting backbones exhibit characteristics typically associated with signed networks such as reciprocity, structural balance and community structure. The developed tool is provided as a free, open-source software package.

This study is carried out in Management Information System (MIS) department which accepts students from general and vocational high schools with widely varying range of educational backgrounds. As an emerging interdisciplinary field, MIS education demands both technical and managerial skills from its students. However, students with different backgrounds have to pursue the same diversified set of courses. The aim of this study is to investigate students' segments and profiles based on the various dimensions of academic abilities they possess, by performing cluster analysis. The data set consists of the student official grade for the required courses. First, dimensionality of the course grades is reduced to a few independent abilities by performing factor analysis. The summed scales representing the independent factors are then used in the cluster analysis to obtain student segments. Finally, variation of the student background measured by high school type is profiled for each segment. The students from general high schools have been more successful in MIS education compared to students from vocational schools where only the basic knowledge on management or computer skills is offered. The results of this analysis are also utilized in shaping various macro and micro level strategies in our MIS department.

Student evaluations to measure the teaching effectiveness of instructor's are very frequently applied in higher education for many years. This study investigates the factors associated with the assessment of instructors teaching performance using two different data mining techniques; stepwise regression and decision trees. The data collected anonymously from students' evaluations of Management Information Systems department at Bogazici University. Additionally, variables related to other instructor and course characteristics are also included in the study. The results show that, a factor summarizing the instructor related questions in the evaluation form, the employment status of the instructor, the workload of the course, the attendance of the students, and the percentage of the students filling the form are significant dimensions of instructor's teaching performance.

Consumer purchasing decision making has been of great interest to researchers and practitioners for improving strategic marketing policies and gaining a competitive advantage in the market.Traditional market models generally concentrate on single individuals rather than taking social interactions into account. However, individuals are tied to one another with invisible bonds and the influence an individual receives from others, affects her purchasing decision which is known as word of mouth (WOM) effect. In this process, some people have greater influence on other consumers' buying decisions that are known as opinion leaders.A new evolving modeling approach, agent-based modeling enables researchers to build models where individual entities and their interactions are directly represented.In this paper, we aim to build an agent-based simulation model for a technological product in a monopolistic artificial market. In particular, we will try to assess the efficiency and profitability of different marketing strategies consisting of different price, promotion, quality levels and different number of targeted opinion leaders where consumers are subject to WOM effects . In the presence of WOM, product's quality is found to be the most significant factor affecting the profit of the company due to the positive WOM effect disseminated by the consumers.