Search Behavior Based Latent Semantic User Segmentation for Advertising Targeting

摘要

The popularity of internet usage greatly motivates the online advertising activities. Compared to advertising on traditional media, online advertising has rich information as well as necessary techniques to achieve precise user targeting. This rich information includes the search behaviors of a user, such as queries issued, or the ads clicked by the user. For popular websites with large number of active users, ad delivery targeting at individual users puts too much burden on the system. User segmentation is an alternative way to relieve this burden by grouping users of similar interests together, then the ad delivery system targets the user segments to display relevant ads, instead of individual users. Existing user segmentation work either adapts clustering methods without considering the hidden semantics embedded in the data, such as K-means, or treats users as data instance and clusters users indirectly even if the latent semantics is incorporated into the transformed data, such as PLSA or LDA. In this paper, we present a search behavior based latent semantic user segmentation method and validate its effectiveness on new ads. Instead of treating users as data instances, they are used as attributes of user issued queries or clicked ads which are considered to be data instances. LDA is then applied to this data set to directly obtain the user segments. Compared to popular K-means clustering, our approach achieves higher CTR values on new ads, with only simple search information.