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Multi-view Click-through Rate Prediction based on Multi-layer Deep Interest Network

With the continuous expansion of Internet coverage, the number of items on e-commerce platforms has exploded. The types of information faced by users are becoming more and more diverse, and so are their interests. How to effectively capture user interests and recommend interesting content to users is the main problem of click-through rate(CTR) prediction task. This paper proposes an interest model for multi-view CTR prediction: MDIN (Multi-layer Deep Interest Networks), which learns the representation of users’ complex interests through a multi-dimensional interest evolution network and a multi-dimensional interest extraction network. This paper evaluates the model approach on real datasets and compares it with multiple state-of-the-art models. Experimental results show that the proposed method outperforms other model methods in the CTR prediction task.