Road drivers performance appraisal using a MCDA method: a case study in the road transport company

Performance evaluation increasingly assumes a more important role in any organizational environment. In the transport area, the drivers are the company’s image and for this reason it is important to develop and increase their performance and commitment to the company goals. This evaluation can be used to motivate driver to improve their performance and to discover training needs. This work aims to create a performance appraisal evaluation model of the drivers based on the multi-criteria decision aid methodology. The MMASSI (Multicriteria Methodology to Support Selection of Information Systems) methodology was adapted by using a template supporting the evaluation according to the freight transportation company in study. The evaluation process involved all drivers (collaborators being evaluated), their supervisors and the company management. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used.

Tuning metadata for better movie content-based recommendation systems

The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.