Using neural word embeddings to model user behavior and detect user segments

Modeling user behavior to detect segments of users to target and to whom address ads (behavioral targeting) is a problem widely-studied in the literature. Various sources of data are mined and modeled in order to detect these segments, such as the queries issued by the users. In this paper we first show the need for a user segmentation system to employ reliable user preferences, since nearly half of the times users reformulate their queries in order to satisfy their information need. Then we propose a method that analyzes the description of the items positively evaluated by the users and extracts a vector representation of the words in these descriptions (word embeddings). Since it is widely-known that users tend to choose items of the same categories, our approach is designed to avoid the so-called preference stability, which would associate the users to trivial segments. Moreover, we make sure that the interpretability of the generated segments is a characteristic offered to the advertisers who will use them. We performed different sets of experiments on a large real-world dataset, which validated our approach and showed its capability to produce effective segments.