This work discusses the ICT solutions designed and developed within the OR.C.HE.S.T.R.A. project. The mission of such an industrial and experimental project (ORganization of Cultural HEritage and Smart Tourism and Real-time Accessibility) consists in developing some technological solutions for tourists and inhabitants aimed at appraising the cultural heritage of the historic center of Naples. The project is based on a Social Innovation approach where services are created engaging all the possible actors in an ecosystem oriented to Smart Culture and tourism (companies, research groups and final users). Thus, in this work some innovative solutions in the cultural heritage domain are promoted and described in order to improve at the same time both the cultural knowledge to offer to different kinds of users (for instance tourists, citizens and researchers) and its learning and its preservation and protection as well. More in detail, we describe how our developed system is able to assist users before visiting the city, by suggesting them the most interesting places to see according to their preferences, and during the visit as well, in order to make the trip more interactive and enjoyable.

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Recommendation systems based on collaborative filtering methods can be exploited in the context of providing personalized artworks tours within a museum. However, in order to be effectively used, several problems have to be addressed: user preferences are not expressed as rating, items to be suggested are located in a physical space, and users may be in a group. In this work, we present a general framework that, by using the Matrix Factorization (MF) approach and a graph representation of a museum, addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. To reach a high-quality initial personalization, the recommendation system uses a simple, but efficient, elicitation method that is inspired by the MF approach. Moreover, the proposed approach considers the individual or the aggregated artworks’ ratings to build up a solution that takes into account the physical location of the artworks

In an Internet of Things vision of smarts museums, recommendation systems based on collaborative filtering approaches can be exploited in the context of providing personalized artworks tours. In this work, we address the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. Differently from a recommender system for an e-commerce application, the problem, here, is trying to maximize the satisfaction of the proposed recommendations, while taking into account an items' ordering that satisfies each group member during the sequence and the artworks location in the museum. In this work, we present a general framework to address such problems and evaluate a prototype implementation with both an offline analysis and a pilot study in a simulated museum environment

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Recent research has shown that explanations serve as an important means to increase transparency in group recommendations while also increasing users' privacy concerns. However, it is currently unclear what personal and contextual factors affect users' privacy concerns about various types of personal information. This paper studies the effect of users' personality traits and preference scenarios -having a majority or minority preference-on their privacy concerns regarding location and emotion information. To create natural scenarios of group decision-making where users can control the amount of information disclosed, we develop Toury-Bot, a chat-bot agent that generates natural language explanations to help group members explain their arguments for suggestions to the group in the tourism domain. We conducted a user study in which we instructed 541 participants to convince the group to either visit or skip a recommended place. Our results show that users generally have a larger concern regarding the disclosure of emotion compared to location information. However, we found no evidence that personality traits or preference scenarios affect privacy concerns in our task. Further analyses revealed that task design (i.e., the pressure on users to convince the group) had an effect on participants' emotion-related privacy concerns. Our study also highlights the utility of providing users with the option of partial disclosure of personal information, which appeared to be popular among the participants. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; User studies; • Information systems → Recommender systems.

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