Sistemi di raccomandazione basati su contesto

The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that computes contextual movie recommendations. Cinemappy refines the recommendation results of a content-based recommender system by exploiting contextual information related to the current spatial and temporal position of the user. The content-based engine leverages DBpedia, one of the best-known datasets publicly available in the Linked Open Data (LOD) project. Context can be defined as “any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves” [10]. This concept is particularly relevant whenever a user needs to look for information that do not depend exclusively from the particular knowledge domain. Thanks to great technological advances occurred in the latest years, particularly in ubiquitous computing, users are able to run almost any kind of application and to perform almost any task on small mobile devices. Smartphones and tablets are becoming a primary platform for information access [22]. If we think of a recommender task in a mobile scenario (e.g., choosing a movie in one of the nearest movie theaters, planning a sightseeing, etc.), we see that most recommendations are requested by users while they are on their way. This causes a continuous change in the context that needs to be carefully addressed. Recommendations are much more useful and enjoyable for end users as they change with their current context [6]. Recommender systems (RS) are information filtering and decision support tools addressing the problem of information overload, providing product and service recommendations personalized for user’s needs and preferences. In this paper we present an implementation of a content-based ContextAware Recommender System (CARS) that gets information needed to recommend items from Linked Open Data (LOD) datasets [14] and combines it with other information freely available on the Web. In particular, our system recommends movies to be watched in theaters that are located in the user’s neighborhood. The recommendation leverages DBpedia [7] as the knowledge base whence we extract movie features, such as genres, actors, directors, subjects, etc.. Main contributions of this paper are: (a) a semantic context-aware recommender system. In our approach we use semantic repositories and the notion of context to provide users with meaningful recommendations in a mobile environment; (b) the exploitation of heterogeneous information sources for movie recommendation. In particular we leverage data coming both from the Web of Data and from the traditional Web; (c) a proof-of-concept Android application exposing geo-location and context-aware features for movie recommendations in user neighborhood. The remainder of this paper is organized as follows. In Section 2 we give some background information about the notion of context in recommender systems and how we exploit it in our approach. In Section 3 we present some basic notions about the usage of DBpedia as knowledge base for recommender systems. Then, in Section 4 we detail our approach and we present our mobile app. In Section 5 we discuss relevant related work. Conclusion and future work close the paper.