Item Multi-Information Evolution Network for Click-Through Rate Prediction

Click-through rate prediction plays a critical role in many fields, and many efforts are devoted to analyzing item behavior as a way to find the interest preferences of target user. Most previous work has not taken into account and fully explored the basic information, historical behavior and temporal information of similar users, which can result in missing information and lead to inaccurate CTR prediction. To solve the above problem, we propose Item Multi-Information Evolution Network (IMIEN). Firstly, for the basic information of users, we use RNN sequences to capture the evolutionary dynamics of users interested in the target item. Secondly, we use Evolutionary Interest Extraction Block (EIEB) to mine the evolutionary interest of target user and similar users over time. Finally, we introduce temporal information to find recent user groups with similar interests as the target user, which aids in predicting the probability of the target user clicking on the target item. We achieve the best results on five public datasets compared with previous mainstream models, which validates the effectiveness of IMIEN.

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