Mining Explainable User Interests from Scalable User Behavior Data

Abstract

Capturing user interests from big user behavior data is critical for online advertising. Based on the user interests, advertisers can significantly reduce their advertising cost by delivering the most relevant ads for the user.

The state-of-the-art user Behavior Targeting (BT) models treat user behaviors as documents, and thus use topic models to extract their interests. A limitation of these methods is that user behaviors are usually described as unexplainable hidden topics, which cannot be directly used to guide online advertising. To this end, we propose in this paper a systematic User Interest Distribution Mining (UIDM for short) Framework to extract explainable user interests from big user behavior data. In the solution, we first use the Probabilistic Latent Semantic Analysis (PLSA) to discover the relationship between users and their behaviors, which can be described as hidden topics. Then, we construct a mapping matrix between the hidden topics and user interests by manually labeling a feature entity matrix. Experiments on real-world data sets demonstrate the performance of the proposed method.