Requirements Engineering is one of the first and most critical processes in system engineering. In this paper we will focus on the collaborative aspects of requirement engineering, in the context of product development. To do so, we adopted the separation of concerns method. Using this method we separate engineering aspects from collaboration aspects in order to study both aspects and finally integrate them. For the collaborative aspect of requirements engineering we looked at Collaboration Engineering. Collaboration Engineering is an approach to design and deploy processes for recurring collaborative tasks that can be transferred to practitioners to execute for themselves without intervention of professional facilitators. From an engineering perspective we will use the requirements engineering processes described by system engineering standard EIA-632 as a starting point. To integrate these we will use methods and techniques from Collaboration Engineering to specify the collaborative processes involved in this requirements engineering approach. An object model was build using Unified Modelling Language (UML). This model shows different concepts underlying our approach. Finally two case studies are presented to evaluate this approach.

This paper focuses on recommender system for agriculture in Mali called SyrAgri. The goal is to guide and improve the quality-of-experience of farmers by offering them good farming practices according to their needs. Two types of recommendations are essentially taken into account: the recommendation of crops and the recommendation of farming practices based on some predefined criteria which are: yield, life cycle of the crop, type of soil, growing season, etc. SyrAgri also informs farmers about crop rotation and the similarity between different types of crops based on the following parameters: crop families, growing seasons and appropriate soil types. For the development of this system a hybrid recommendation approach was used: demographic, semantic and collaborative methods. Each method is adapted to a specific stage of a user’s visit to the system. The demographic approach is first activated in order to offer recommendations to new users of the system, which resolves the concept of cold start (immediate inclusion of a new item or a new user in the system). The semantic approach is then activated to recommend to the user items (crops, agricultural practices) semantically close to those (s)he has appreciated. Finally, the collaborative approach is used to recommend items that similar users have liked.

Recommender systems aim to support decision-makers by providing decision advice. We review briefly tools of Multi-Criteria Decision Analysis (MCDA), including aggregation operators, that could be the basis for a recommender system. Then we develop a multi-criteria recommender system, STROMa (SysTem of RecOmmendation Multi-criteria), to support decisions by aggregating measures of performance contained in a performance matrix. The system makes inferences about preferences using a partial order on criteria input by the decision-maker. To determine a total ordering of the alternatives, STROMa uses a multi-criteria aggregation operator, the Choquet integral of a fuzzy measure. Thus, recommendations are calculated using partial preferences provided by the decision maker and updated by the system. An integrated web platform is under development.

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