Recommender systems support users in finding the appropriate information at the correct time. While traditional recommenders only take into account the stable preferences of target users (ego-based interests), some recent approaches in the area of social recommender systems have started to acknowledge that the mere fact of taking part in social relationships may cause individuals to modify their attitudes and behaviors, and have proposed methods for generating recommendations based on the preferences of the target users ’ social networks (network-based interests). However, little work has investigated how to effectively merge ego- and network-based interests. In this paper, we present SoNARS++, an advanced social algorithm that assesses the interest for an item to be recommended by combining a user’s personal interests for that item with the interests for that item of the user’s social network, depending on its structure and on social influence relationships among users. The results of the experimental evaluation we carried out show that SoNARS++ is comparable for its precision to a mainstream algorithm such as collaborative filtering but appears to provide users with more useful recommendations. RESEARCH HIGHLIGHTS • SoNARS++ as advanced social algorithm to generate recommendations for users. • It combines user personal preferences and preferences of the user social network. • It has comparable precision to collaborative filtering, but higher usefulness.

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

Wanteat is a framework and a suite of applications which allow users to interact with and explore mixed social networks of smart objects and people in the gastronomy domain, thus promoting the cultural heritage of a territory. Wanteat interaction model is based on the concept of a "wheel" [1].

This position paper describes the role ontologies can play in Mobile Context-Aware recommender systems. In a Semantic Web vision of recommender systems, the adoption of ontologies for modeling the domain, the context and the adaptation process can contribute to tailor the right information/service to users and thus facilitate the user-system interaction and the system communication with other agents.