The existence of creativity support tools establishes creativity as part of the Computer Science research. Therefore, the development of computational methods for the enhancement of creativity is undoubtedly a challenge. In this paper we argue that adding context awareness in creativity support tools will enhance the creativity process. This belief is based on the evaluation of a number of the most popular creativity support tools in relation to the features and characteristics they support. In this review, we examine the characteristics related to the interaction between the user and the creativity support tools in two phases: the 'preparation' of creativity process and the 'ideation' phase. Through this analysis we observe that the tools in most cases play a passive role. Real time Human-Computer interaction is missing, and therefore the creativity process is not as effective as it could and should be. Finally, we conclude that the addition of context awareness in creativity support tools can enhance the creativity process and innovation.

Learning can be observed in the creativity process. When this process is supported by a Creativity Support Tool (CST), considering the context in which ideas are developed, as well as the context around the user himself and the task he is carrying out can potentially enhance creativity.The tool’s awareness of such context can be exploited in the offering of useful context-aware recommendations to the users on topics such as relevant resources, people, ideas, projects, et cetera. These recommendations can help users during the creativity process and the learning involved, by providing productive stimuli. In the work presented in this chapter we focus on describing a method for enhancing the creativity process through context-aware recommendations. The method uses ontologies for the knowledge representation of context and the topic maps technology for storing, managing, and delivering content used as recommendations. Furthermore we present the software system that has been developed to support this method in a particular collaborative CST, as well as its evaluation.

This work describes the design, development and evaluation of a software Prototype, named ArchReco, an educational tool that employs two types of Context-aware Recommendations of Design Patterns, to support users (CS students or professionals) who want to improve their design skills when it comes to training for High Level Software models. The tool's underlying algorithms take advantage of Semantic Web technologies, and the usage of Content based analysis for the computation of non-personalized recommendations for Design Patterns. The recommendations' objective is to support users in functions such as finding the most suitable Design Pattern to use according to the working context, learn the meaning, objectives and usages of each Design Pattern. The current work presents the Semantic Modeling of the Software Design process through the definition of the context that defines the Software Design process and in particular the representation of the Design Patterns as Ontology model, the implemented Context Aware Recommendation Algorithms and the evaluation results extracted from a user based testing for the ArchReco prototype.

Recommender or recommendation systems are software tools that make useful suggestions to users, by taking into account their profile, preferences and/or actions during interaction with an application or website. They are usually personalized and can refer to items to buy, people to connect to or books/ articles to read. Recommender Systems (RS) aim at helping users with their interaction by bringing to surface the information that is relevant to them, their needs, or their tasks. This article's objective is to present a review of the different types of RS, the techniques and methods used for building such systems, the algorithms used to generate the recommendations and how these systems can be evaluated. Finally, a number of topics are discussed as envisioned future research directions.

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