Cross-Domain Wireless Software Defined Network Based Recommendation

Irina Lebedeva, Fan Wu, Guihai Chen

Abstract: New developments in mobile computing, wireless networking and social networking leverage opportunities to create location-based social networks (LBSNs) such as Foursquare, Gowalla, Facebook Place. Pointof-interest (POI) recommendation is a significant task in LBSNs, since it provides recommendations that respect personal preferences and capture social and environmental parameters. Moreover, employing Wireless Sensor Networks (WSNs) in POIs enables to improve recommendation accuracy. Most of existing POI recommendation methods deals with the only single domain, whereas the use of information available from other application domains potentially provides better POI recommendation results. This is particularly relevant for the newly launched LBSNs, where little check in information is available. In this paper, to the best of our knowledge, we are the first to propose a cross domain POI recommendation method in LBSNs and apply transfer learning for this purpose. To this end, we first incorporate both user and POI knowledge in auxiliary domains by discovering the principle coordinates. Next, we transfer them to the target domain and then make predictions using the transferred knowledge. The experimental results demonstrate high performance of our method and the merits of cross-domain POI recommendation in LBSNs.

External IDs:dblp:conf/msn/LebedevaWC16