Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing

(processions, special markets, festivals, and so on) based on a theoretical model and the transitive closure computation. The method was, then, implemented, in a knowledge-based recommender system prototype, named T-Path, which is able to suggest event itineraries in Apulia region (in southern Italy

Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC

argumentation evolved as a successful approach in AI to model commonsense qualitative reasoning with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents ArgueNet, a recommender system that classifies search results according to preference criteria

domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources

User preferences change over time and capturing such changes is essential for developing accurate recom-mender systems. Despite its importance, only a few works in collaborative filtering have addressed this is-sue. In this paper, we consider evolving preferences and we model user dynamics

mining, semantics, artificial intelligence, user modeling and profiling, etc. setting up a successful recommendation system is not a trivial or straightforward task. This paper argues that by monitoring, analyzing and understanding the behavior of customers, their demographics, opinions, preferences

. In this paper, we present a case model exploited in a mobile critique-based recommender system that generates recommendations using the knowledge contained in previous recommendation cases. The proposed case model is capable of modeling evolving (conversational) recommendation sessions, capturing

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this paper we present an approach for interactive B2C eCommerce systems that support the required guided transition from a needs- to a feature-oriented interaction. The approach distinguishes between a model level and a system level (see Figure 1). On the model level, this approach suggests novel