Group Representation and Profiling

In order to satisfy increasing needs for service personalization, mastering the knowledge of individual user profiles is no longer sufficient. Indeed, there exist numerous services which are consumed in a social or virtual environment. For example, to provide personalization services with a real added-value for interactive IPTV, its content needs to be adapted (through VoD/program mosaic) to different tastes and interests of the viewers' group (family members, friends, etc.). In a different context, to increase the ROI of advertisers, the digital billboards need to dynamically adapt their content to the surrounding group of individuals. Similar needs are also present in virtual spaces like web conferences, chat rooms or social networking applications. In all such environments, the personalization technology has to go beyond individual adaptive systems by bringing in group profiling and group recommendation systems, the intelligence that allows to conciliate potentially conflicting user interests, needs and restrictions [Jameson, 2004; O'Connor et al., 2001]. There exist several systems providing recommendations of miscellaneous items to a group of users [Masthoff, 2011]. Examples of best known systems are: Polylens which is an extension of MovieLens and provides movie recommendations, MusicFX [McCarthy, 1998] which chooses a radio station in a fitness center according to visitors present at a given moment, Intrigue which recommends places to visit to a group of tourists and Yu's TV Recommender which, as its name shows, proposes a TV program to a group of viewers.