MLIM: A CTR prediction model describing evolution law of user interest

Abstract

With the advent of the digital economy era, business systems such as web advertising and recommendation system have put forward the demand for predicting the click through rate (CTR) of items. However, the current CTR prediction research is not enough to mine user behavior, resulting in the lack of accuracy of user interest representation. In this paper, we propose a CTR prediction model, called MLIM, which can deep mine the evolution law of user interest. Specifically, we first use BiGRU to obtain the low-level user interest representation in the interest extraction layer, and then continue to use attention mechanism, BiGRU and sliding time window multi-components collaborative modeling in the interest evolution layer to obtain multi-level user interest representation with richer information, which can improve the accuracy of CTR prediction to a certain extent. Comprehensive experiments on two real datasets show that the proposed model achieves better performance than the mainstream baselines integrating user behavior analysis.