social filtering methods [18]. While the use of this kind of Personal: this component holds user's personal data such as recommendations is not uninteresting(we also embrace them name and e-mail: n this very work), it is clear that the user itself is still the most Demographics: this information will be used by knowle important and primary source of recommending material, one discovery techniques, such as Data Mining whose deep analysis has not yet been performed in such a algorithms, in order to find usage patterns which may be useful for the system as well. Demographic data may also be used as part of the rs; is with some surprise that systems do not rely on information Handicaps: this group of user physical information is related to the psychological behavioral side of users, a key responsible for avoiding suggestions that do not fit a certain component of decision-making processes user handicap Given this action space for improvement, the proposed Trips: this module contains work tries to evolve UM techniques and the way systems can trip in which the user might be involved in, as well as the benefit from different kinds of user information, by using a containing tours. This module also contains past trips complex UM framework. IIL. DEvELOPment B. User Reasoning /Inference Mechanisms As it was earlier implied, a powerful UM process is he project to be described here devises a UM methodology endorsed by several components that maximize the hich dictates how the system is designed and operates: accuracy User Modeling(UM)-it's the means by which a system of the necessary user information in a variety of ways. Since with the final purpose of improving and customizing user methodology must therefore be a collaborative effort of begins with a suitable representation of the user (or user of part of user data. Furthermore, it is also believed that model, which can be the sum of a wide variety of different knowing a certain user information space by using more than techniques); then, that information is used to infer valuable one method simultaneously successfully increases confidence knowledge that can either be added to the user model or used in existent assumptions and divides responsibility amongst by the last phase of the process, the system adaptation. Fig. 1 various techniques, which ultimately results in a more backed- shows such as cycle up system with more solutions [344][5] User Model Figure 1. User Modeling Process The rest of this section will follow this approach, " UM as a Process", namely with the specifics of both the first and second stage of the platform. A. User model The User Model is the root of the UM process and pertains Figure 3. Knowledge Discovery Mechanisms Architecture to the broader user architecture that it features. In a certain point in time, the user model photogram is the user image as Fig. 3 allows the easy inference that tourism applications perceived by the system. The created model got part of its upper-level core functions, such as the RS, gather information that data into a coherent the information hierarchy that's comprehended in the proposed user profile, in order to generate new information User Model Community Models(CM). CM are an adaptation of etworks(NN), specifically simple form of previously fed into its mechanisms [8]. The first CM is about system navigation(user sessions, clickstream analysis, etc. ) Figure 2. User Model Architecture commitment, such as the generating a route. Plus, CM still display the following advantages: (1)act as a clusteringsocial filtering methods [18]. While the use of this kind of recommendations is not uninteresting (we also embrace them in this very work), it is clear that the user itself is still the most important and primary source of recommending material, one whose deep analysis has not yet been performed in such a complete manner as will be presented. To finish, and in such a domain where both personal and social interests are at stake, it is with some surprise that systems do not rely on information related to the psychological / behavioral side of users, a key component of decision-making processes. Given this action space for improvement, the proposed work tries to evolve UM techniques and the way systems can benefit from different kinds of user information, by using a complex UM framework. III. DEVELOPMENT The project to be described here devises a UM methodology which dictates how the system is designed and operates: User Modeling (UM) - it’s the means by which a system keeps user data and uses that information in a variety of ways with the final purpose of improving and customizing user experience within that system. It pertains to a process that begins with a suitable representation of the user (or user model, which can be the sum of a wide variety of different techniques); then, that information is used to infer valuable knowledge that can either be added to the user model or used by the last phase of the process, the system adaptation. Fig. 1 shows such as cycle. Figure 1. User Modeling Process The rest of this section will follow this approach, “UM as a Process”, namely with the specifics of both the first and second stage of the platform. A. User Model The User Model is the root of the UM process and pertains to the broader user architecture that it features. In a certain point in time, the user model photogram is the user image as perceived by the system. The created model got part of its influence from Benyon’s UM architecture [15]. Fig. 2 presents the information hierarchy that’s comprehended in the proposed user model. Figure 2. User Model Architecture • Personal: this component holds user’s personal data such as name and e-mail; • Demographics: this information will be used by knowledge discovery techniques, such as Data Mining (DM) algorithms, in order to find usage patterns which may be useful for the system as well. Demographic data may also be used as part of the RS; • Handicaps: this group of user physical information is responsible for avoiding suggestions that do not fit a certain user handicap. • Trips: this module contains information about the current trip in which the user might be involved in, as well as the containing tours. This module also contains past trips. B. User Reasoning / Inference Mechanisms As it was earlier implied, a powerful UM process is endorsed by several components that maximize the accuracy of the necessary user information in a variety of ways. Since user data can’t all be retrieved in the same manner, our UM methodology must therefore be a collaborative effort of several sub-systems, each of them responsible for the retrieval of part of user data. Furthermore, it is also believed that knowing a certain user information space by using more than one method simultaneously successfully increases confidence in existent assumptions and divides responsibility amongst various techniques, which ultimately results in a more backed￾up system with more solutions [3][4][5].                      !   " Figure 3. Knowledge Discovery Mechanisms’ Architecture Fig. 3 allows the easy inference that tourism applications’ upper-level core functions, such as the RS, gather information throughout all sub-systems and merge that data into a coherent user profile, in order to generate new information. 1) Community Models Our UM architecture makes use of a group of two Community Models (CM). CM are an adaptation of Neural Networks (NN), specifically a rather simple form of those. CM generate two-dimensional representations of the data previously fed into its mechanisms [8]. The first CM is about system navigation (user sessions, clickstream analysis, etc.), while the second is concerned about effective POI commitment, such as the generating a route. Plus, CM still display the following advantages: (1) act as a clustering 620