Traditionally, Group Recommender Systems (GRS) apply an aggregation approach, which computes a group rating for each item by estimating unknown individual ratings, for which has been demonstrated that matrix factorization (MF) models are superior to classic nearest-neighbor techniques in individual recommender systems. Moreover, when people are in a group making a choice from alternatives, they tend to change their opinions accordingly to the social influence exerted by others' group members. Sociological analyses suggest that some social factors express social influence in a group, such as, cohesion, social similarity and social centrality. In this work, we combine a MF model to estimate unknown ratings with a social network analysis (SNA) to evidence possible social influence. Firstly, we present an analysis of the relevance of social factors detected in relation with the members' opinions and, then, we describe the results obtained when comparing the proposed technique with the classic group recommender technique.

The Software Architecture is an important asset in a software development process, which serves to share and discuss the main design concerns among the project stakeholders. The architecture must be properly documented (e.g., via a Wiki environment) to be effectively used by these stakeholders. However, the process of producing architecture documentation often fails to deliver contents that address the stakeholders’ information needs. To address the problem, we argue for a knowledge management strategy in which: (i) architecture documentation is created incrementally; and (ii) its contents are driven by a model of stakeholder preferences. In this work, we present an information optimization approach applied to the architecture documentation domain, derived from an existing documentation method called Views & Beyond. To do so, we define the Next SAD Version Problem (NSVP) and then provide tool support to assist architects in producing cost-effective documentation. Based on prior work, we perform a sensitivity analysis of the optimization model and develop a robust formulation that takes into account uncertainty in the parameter estimations for NSVP instances, thus improving the outcomes of our documentation assistant.