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The development of online social networks enables users to generate a large amount of historical behavior data. How to effectively extract user interests from these data is crucial to improve the performance of click-through rate (CTR) prediction. However, existing research ignores the importance of collaborative personalized interest extraction based on the dynamic relationship in CTR prediction, which will cause the model to lose some important information to improve performance. Therefore, this paper designs an adaptive dynamic extraction (ADE) Unit to obtain user interest representations. First, the ADE unit designs a time weighting function to consider the dynamic evolution of user interests; then, the user’s personalized interest representation is generated for a specific item. Finally, collaborative users are used to obtain the interest intensity of newly interested users. Based on the ADE unit, a new CTR prediction model is proposed, called personalized collaborative interest extraction network (PC-IEN). The model design feature embedding layer obtains a low-dimensional representation vector. Mini-batch aware regularization and the Dice activation function are employed to train many parameters of the network. The experimental results on three real datasets show that compared with other algorithms, the model can improve AUC by at least 0.11% and GAUC by 0.2%, indicating that the proposed algorithm outperforms other models in performance.
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