Nowadays, Recommender Systems have become essential to users for finding “what they need” within large collections of items. Meanwhile, recent studies have demonstrated as user personality can effectively provide a more valuable information to significantly improve recommenders' performance, especially considering behavioural data captured from social network logs. In this work, we describe a novel music recommendation technique based on the identification of personality traits, moods and emotions of a single user, starting from solid psychological observations recognized by the analysis of user behavior within a social environment. In particular, users personality and mood have been embedded within a content-based filtering approach to obtain more accurate and dynamic results. Several experiments are then reported to show effectiveness of user personality and mood recognition recommendation, thus encouraging research in this direction.

The extraordinary technological progress we have witnessed in recent years has made it possible to generate and exchange multimedia content at an unprecedented rate. As a consequence, massive collections of multimedia objects are now widely available to a large population of users. As the task of browsing such large collections could be daunting, Recommender Systems are being developed to assist users in finding items that match their needs and preferences. In this article, we present a novel approach to recommendation in multimedia browsing systems, based on modeling recommendation as a social choice problem. In social choice theory, a set of voters is called to rank a set of alternatives, and individual rankings are aggregated into a global ranking. In our formulation, the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We first define what constitutes a choice in the browsing domain and then define a mechanism to aggregate individual choices into a global ranking. The result is a framework for computing customized recommendations by originally combining intrinsic features of multimedia objects, past behavior of individual users, and overall behavior of the entire community of users. Recommendations are ranked using an importance ranking algorithm that resembles the well-known PageRank strategy. Experiments conducted on a prototype of the proposed system confirm the effectiveness and efficiency of our approach.

In this paper, we present an ongoing project, named Talking Museum and developed within DATABENC - a high technology district for Cultural Heritage management. The project exploits the Internet of Things technologies in order to make objects of a museum exhibition able to “talk” during users’ visit and capable of automatically telling their story using multimedia facilities. In particular, we have deployed in the museum a particular Wireless Sensor Network that, using Bluetooth technology, is able to sense the surrounding area for detecting user devices’ presence. Once a device has been detected, the related MAC address is retrieved and a multimedia story of the closest museum objects is delivered to the related user. Eventually, proper multimedia recommendation techniques drive users towards other objects of possible interest to facilitate and make more stimulating the visit. As case of study, we show an example of Talking museum as a smart guide of sculptures’ art exhibition within the Maschio Angioino castle, in Naples (Italy)

In this paper, we describe the project SNOPS, a smart-city environment based on Future Internet technologies. We fo- cused on the context-aware recommendation services pro- vided in the platform, which accommodates location depen- dent multimedia information with user's needs in a mobile environment related to an outdoor scenario within the Cul- tural Heritage domain. In particular, we describe a recom- mendation strategy for planning browsing activities exploit- ing objects features, users' behaviors and context informa- tion gathered by apposite sensor networks. Preliminary ex- perimental results, related to user's satisfaction, have been carried out and discussed