Click-through conversion rate prediction model of book e-commerce platform based on feature combination and representation

The prediction of click-through conversion rates has always been an integral focus within e-commerce platforms. This paper proposes a novel prediction model that leverages feature combination and representation learning techniques, centering around three crucial elements of the Buyer-Book-Activity (BBA) e-commerce platform. Specifically, our approach considers the object characteristics of book e-commerce platforms. Firstly, we create a comprehensive three-part BBA diagram encompassing user-book-marketing activities and thoroughly describe the book e-commerce platform's information space. To construct a comprehensive feature space, we employ a factor decomposition method, enabling us to combine and mine large-scale information from the BBA. In particular, we introduce book2tag text features to capture textual information's hidden richness and decompose it. Secondly, to represent the overall information space of the book e-commerce platform, we propose the BBA2vec representation learning method. This method captures the complexity and diversity of the BBA's effective data feature space. This is crucial as feature combinations alone may not fully capture the diversity of e-commerce features. Finally, a deep neural network is used to predict click-through conversion rates by combining feature combination and feature representation. The experimental results show that the model accurately predicts the bookstore e-commerce platform's click-purchase conversion rate.