From Recommendation to Profile Inference (Rec2PI)
Cheng Chen, Fang Dong, Kui Wu, Venkatesh Bharadwaj Srinivasan, Alex Thomo · 2016
Portable smart devices have become prevalent and are used for ubiquitous access to the Internet in our daily life. Taking advantage of this trend, brick-and-mortar retailers have been increasingly deploying free Wi-Fi hotspots to provide easy Internet access for their customers. This opens the opportunity for retailers to collect customer information and perform data mining to improve the quality of their service. In this paper, we propose a novel value-added service to Wi-Fi data mining, Rec2PI, which can infer users' preference profiles based on recommendations pushed by third-party apps. Such profiles can be used to improve users' online experience and enable a brick-and-mortar retailer to participate in the global advertising business. Since the goal and technical difficulties of Rec2PI significantly differ from those of traditional recommender systems, we present a general framework of Rec2PI to illustrate its process. To tackle the technical challenges in profile inference, we propose novel algorithms built using copulas, a statistical tool suitable for capturing complex dependence structure beyond the scope of linear dependence. In the context of rating-based recommendations, we evaluate the proposed algorithms using an open dataset and a real-world recommender system. The evaluation results show that Rec2PI creates consistent and accurate inference results.