User sequential behavior classification for click-through rate prediction

The user’s behavior sequence can well reflect the user’s interest and intention, but there is less research on the use of user behavior sequence in the field of CTR prediction. Therefore, introducing the idea of sequence recommendation into CTR prediction is an exciting idea. Aimless browsing by users is a common phenomenon in many recommended scenarios (e-commerce, music, video streaming), which has not been paid attention to in previous research. To this end, this paper introduces the concept of user browsing status, and divides it into Discover and Intent, which respectively represent the user’s unintentional status and intentional status. A new framework named User Status Recognition Framework (USRF) is proposed to solve this problem. USRF can perform both CTR prediction and next-item recommendation. The framework captures the weights of two user status from the current user historical behavior sequence, models the two different status separately to mine user interests, and combines the captured user status to make more accurate recommendations. In addition, in order to solve the problem that less attention has been paid to the complex connection between candidate items and interacted items in the previous sequence recommendation research, this paper uses the attention mechanism to model the relationship between candidate items and interacted items, implements a simple model for CTR prediction based on USRF. Experiments on three different scene datasets show good results on both the AUC and F1-score, proving the advantages of the framework.