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Gürkan Alpaslan

By the growing trend of online shopping and e-commerce websites, recommendation systems have gained more importance in recent years in order to increase the sales ratios of companies. Different algorithms on recommendation systems are used and every one produce different results. Every algorithm on this area have positive and negative attributes. The purpose of the research is to test the different algorithms for choosing the best one according as structure of dataset and aims of developers. For this purpose, threshold and k-means based collaborative filtering and content-based filtering algorithms are utilized on the dataset contains 100*73421 matrix length. What are the differences and effects of these different algorithms on the same dataset? What are the challenges of the algorithms? What criteria are more important in order to evaluate a recommendation systems? In the study, we answer these crucial problems with the case study.

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Model driven development is an effective method due to its benefits such as code transformation, increasing productivity and reducing human based error possibilities. Meanwhile, agile software development increases the software flexibility and customer satisfaction by using iterative method. Can these two development approaches be combined to develop web applications efficiently? What are the challenges and what are the benefits of this approach? In this paper, we answer these two crucial problems; combining model driven development and agile software development results in not only fast development and easiness of the user interface design but also efficient job tracking. We also defined an agile model based approach for web applications whose implementation study has been carried out to support the answers we gave these two crucial problems.