A latent semantic pattern recognition strategy for an untrivial targeted advertising

Target definition is a process aimed at partitioning the potential audience of an advertiser into several classes, according to specific criteria. Almost all the existing approaches take into account only the explicit preferences of the users, without considering the hidden semantics embedded in their choices, so the target definition is affected by widely-known problems. One of the most important is that easily understandable segments are not effective for marketing purposes due to their triviality, whereas more complex segmentations are hard to understand. In this paper we propose a novel segmentation strategy able to uncover the implicit preferences of the users, by studying the semantic overlapping between the classes of items positively evaluated by them and the rest of classes. The main advantages of our proposal are that the desired target can be specified by the advertiser, and that the set of users is easily described by the class of items that characterizes them, this means that the complexity of the semantic analysis is hidden to the advertiser, and we obtain an interpretable and non-trivial user segmentation, built by using reliable information. Experimental results confirm the effectiveness of our approach in the generation of the target audience.