Hidden POI Ranking with Spatial Crowdsourcing

Exploring Hidden Points of Interest (H-POIs), which are rarely referred in online search and recommendation systems due to insufficient check-in records, benefits business and individuals. In this work, we investigate how to eliminate the hidden feature of H-POIs by enhancing conventional crowdsourced ranking aggregation framework with heterogeneous (i.e., H-POI and Popular Point of Interest (P-POI)) pairwise tasks. We propose a two-phase solution focusing on both effectiveness and efficiency. In offline phase, we substantially narrow down the search space by retrieving a set of geo-textual valid heterogeneous pairs as the initial candidates and develop two practical data-driven strategies to compute worker qualities. In the online phase, we minimize the cost of assessment by introducing an active learning algorithm to jointly select pairs and workers with worker quality, uncertainty of P-POI rankings and uncertainty of the model taken into account. In addition, a (Minimum Spanning) Tree-constrained Skip search strategy is proposed for the purpose of reducing search time cost. Empirical experiments based on real POI datasets verify that the ranking accuracy of H-POIs can be greatly improved with small number of query iterations.