Mining social networks for local search and location-based recommender systems

Location-based services (LBSs) are software-level services that use location data in order to provide interesting and useful content to users or other services. The widespread usage of smartphones and wearable devices has made available large amounts of spatio-temporal data (e.g., geolocation, motion and environmental sensors). This new reality opens up compelling opportunities and raises challenges related to the automatic discovery and interpretation of data in pervasive environments. For instance, context-aware recommender systems (CARS) aggregate situational and environmental information about people, places and activities to satisfy immediate needs and offer enriched, situation-aware content, services and experiences. Popular applications are tourist tour planning and music recommender systems. While contextual factors quickly became the key of success of these pervasive applications, information related to user interests and preferences as well as social signals have not yet been adequately capitalized. The massive adoption of social applications, including social network services (e.g. Facebook and Twitter), collaborative tagging systems (e.g. Flickr and Delicious) and online communities (e.g. Foursquare and Yelp) gathers a wealth of social interactions between users, or between users and shared resources (e.g., points of interest, movies). Social local search and recommendation often refers to the search and recommendation paradigms affected by explicit or inferred social signals. The former are identified in the user’s personal circle of friends, relatives or colleagues (egocentric network); the latter arise from groups of users that share common interests and behaviors (sociocentric network), even if no explicit ties bind them. Within this context, techniques employed for data and text mining, social network analysis and community detection, sentiment analysis and opinion mining have the chance to generate more accurate recommendations and personalized services. For instance, they can help us to understand more of the users’ collective behavior by clustering similar users with respect to their interests, preferences and activities; or by recognizing knowledge experts, namely, users that are generally more capable than others in finding out relevant content and that therefore could play the role of trustworthy opinion sources. The aim of this special issue is to explore recent advances in Local Search (LS) and Location-based Recommender Systems (LRS) focusing on the value, impact and implications of the analysis of social signals to alleviate information and interaction overload by filtering the most attractive and relevant content. This special issue has solicited original research contributions from academia and industry in the form of theoretical foundations, experimental and methodological developments, comparative analyses, descriptive surveys, experiments and case studies in the field. * Fabio Gasparetti gaspare@dia.uniroma3.it