Uncovering poiesis : the role of production in technical communication, work, and public life

This dissertation is a report on a collaborative project between the Computer Science and the Humanities Departments to develop case studies that focus on issues of communication in the workplace, and the results of their use in the classroom. My argument is that case study teaching simulates real-world experience in a meaningful way, essentially developing a teachable way of developing phronesis, the reasoned capacity to act for the good in public. In addition, it can be read as a "how-to" guide for educators who may wish to construct their own case studies. To that end, I have included a discussion of the ethnographic methodologies employed, and how it was adapted to our more pragmatic ends. Finally, I present my overarching argument for a new appraisal of the concept of techné. This reappraisal emphasizes its productive activity, poiesis, rather than focusing on its knowledge as has been the case in the past. I propose that focusing on the telos, the end outside the production, contributes to the diminishment, if not complete foreclosure, of a rich concept of techné.

A COMPARATIVE STUDY AND EVALUATION OF COLLABORATIVE RECOMMENDATION SYSTEMS

Consumers currently enjoy a surplus of goods (books, videos, music, or other items) available to purchase. While this surplus often allows a consumer to find a product tailored to their preferences or needs, the volume of items available may require considerable time or effort on the part of the user to find the most relevant item. Recommendation systems have become a common part of many online business that supply users books, videos, music, or other items to consumers. These systems attempt to provide assistance to consumers in finding the items that fit their preferences. This report presents an overview of recommendation systems. We will also briefly explore the history of recommendation systems and the large boost that was given to research in this field due to the Netflix Challenge. The classical methods for collaborative recommendation systems are reviewed and implemented, and an examination is performed contrasting the complexity and performance among the various models. Finally, current challenges and approaches are discussed.