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Personality trait detection is a problem that has been gaining much attention in the computer science field recently. By leveraging users’ personality knowledge software applications are able to adapt their behaviour accordingly. To detect personality traits automatically users must substantially interact with software applications to gather enough information that describe their behaviour. For addressing this limitation, the authors explore the use of online video games as an alternative approach to detect personality dichotomies. The paper aims to discuss these issues.

The authors analyse the use of several online video games that exhibit features related with Myers-Briggs sensitive-intuitive personality dichotomy. Then, the authors build a user profile that describes users’ behaviour when interacting with online video games. Finally, the authors identify users’ personality by analysing their profile with different classification algorithms.

The results show that games that obtained better results in the personality dichotomy detection exhibit features that had better match with the sensitive-intuitive dichotomy preferences. Moreover, the results show that the classification algorithms should satisfactorily deal with unbalanced data sets, since it is natural that the frequencies of the dichotomies types are unbalanced. In addition, in the context of personality trait detection, online video games possess several advantages over other type of software applications. By using games, users do not need to have previous experience, since they learn how to play during gameplay. Furthermore, the information and time needed to predict the sensitive-intuitive dichotomy using games is little.

This study shows that online video games are a promising environment in which the users’ personality dichotomies can be detected.

Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce, movie recommendation, tourism, restaurant recommendation, among others. Despite the various and different domains in which recommender agents are used and the variety of approaches they use to represent user interests and make recommendations, there is some functionality that is common to all of them, such as user model management and recommendation of interesting items. This paper aims at generalizing these common behaviors into a framework that enables developers to reuse recommender agents' main characteristics in their own developments.

This work presents a framework for recommendation that provides the control structures, the data structures and a set of algorithms and metrics for different recommendation methods. The proposed framework acts as the base design for recommender agents or applications that want to add the already modeled and implemented capabilities to their own functionality. In contrast with other proposals, this framework is designed to enable the integration of diverse user models, such as demographic, content‐based and item‐based. In addition to the different implementations provided for these components, new algorithms and user model representations can be easily added to the proposed approach. Thus, personal agents originally designed to assist a single user can reuse the behavior implemented in the framework to expand their recommendation strategies.

The paper describes three different recommender agents built by materializing the proposed framework: a movie recommender agent, a tourism recommender agent, and a web page recommender agent. Each agent uses a different recommendation approach. PersonalSearcher, an agent originally designed to suggest interesting web pages to a user, was extended to collaboratively assist a group of users using content‐based algorithms. MovieRecommender recommends interesting movies using an item‐based approach and Traveller suggests holiday packages using demographic user models. Findings encountered during the development of these agents and their empirical evaluation are described here.

The advantages of the proposed framework are twofold. On the one hand, the functionality provided by the framework enables the development of recommender agents without the need for implementing its whole set of capabilities from scratch. The main processes and data structures of recommender agents are already implemented. On the other hand, already existing agents can be enhanced by incorporating the functionality provided by the recommendation framework in order to act collaboratively.