Interactive Attention-Based Capsule Network for Click-Through Rate Prediction

With the continuous penetration of Internet applications in our lives, the ever-increasing data on clicking behavior has made online services a critical component of the economic sectors of internet companies over the past decade. This development trend has brought a large amount of information that reflects user needs but is relatively chaotic. Extracting user interests and needs from complex click behaviors is crucial for advancing online business development and precisely targeting product information The interactive attention-based capsules (IACaps) network is proposed in this paper to collate and analyze complex and changing click information for user behavior representation. Specifically, an interactive attention dynamic routing mechanism is proposed to mine the potential association information among different browsing behaviors, which facilitates the extraction and understanding of seemingly irrelevant information hidden in massive click data. To ensure the practicability of the proposed method, three different types of datasets were selected from Amazon Dataset for experiments, and the results of which shows the superior performance of the proposed method when compared with other models. Specifically, the reasonableness and effectiveness of the reported model are further proved by improvements of metrics obtained in the main experiments and ablation studies. Optimization of Hyper-parameters is also analyzed from the number of iterations, the number of capsules, and the dimension of capsules for better understanding of operating principles.