Location-aware computing to mobile services recommendation: Theory and practice

Abstract

In recent years, many daily web/app services (e.g. Facebook, Twitter, and Foursquare) generate data and traces that are often transparently annotated with location and contextual information. Many core challenges are involved to fully exploit geo-labeled data. The main challenge is to combine ideas and techniques from various research communities, such as recommender systems, data management, geographic information systems, social network analytics, and text mining. We aim to provide a platform to discuss indepth and collecting feedback about challenges, opportunities, novel techniques, and applications. Finally, we have four papers for this special issue. A summary of these papers is outlined below. In the paper entitled “Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation”, Minsung Hong and Jason J. Jung propose a multi-criteria tensor model combining spatial and temporal information in the recommender systems. Specifically, the five-order tensor model consists of users, items, multiple ratings, spatial and temporal data, which keeps the latent structure of the interrelations between multi-criteria and spatial/temporal information. Experimental results with a TripAdvisor dataset show that the proposed model outperforms other baselines. In the paper entitled “A mobile services recommendation system fuses implicit and explicit user trust relationships”, Pengcheng Luo, Jilin Zhang,