Binary sieves: Toward a semantic approach to user segmentation for behavioral targeting

Behavioral targeting is the process of addressing ads to a specific set of users. The set of target users is detected from a segmentation of the user set, based on their interactions with the website (pages visited, items purchased, etc.). Recently, in order to improve the segmentation process, the semantics behind the user behavior has been exploited, by analyzing the queries issued by the users. However, nearly half of the times users need to reformulate their queries in order to satisfy their information need. In this paper, we tackle the problem of semantic behavioral targeting considering reliable user preferences, by performing a semantic analysis on the descriptions of the items positively rated by the users. We also consider widely-known problems, such as the interpretability of a segment, and the fact that user preferences are usually stable over time, which could lead to a trivial segmentation. In order to overcome these issues, our approach allows an advertiser to automatically extract a user segment by specifying the interests that she/he wants to target, by means of a novel boolean algebra; the segments are composed of users whose evaluated items are semantically related to these interests. This leads to interpretable and non-trivial segments, built by using reliable information. Experimental results confirm the effectiveness of our approach at producing users segments.