Context-aware cross feature attentive network for click-through rate predictions

Click-through rate (CTR) prediction aims to estimate the likelihood that a user will interact with an item. It has gained significant attention in areas such as online advertising and e-commerce. Existing studies have verified that feature interactions play a crucial role in CTR prediction, highlighting the need for efficient modeling of these interactions. However, most existing approaches in CTR prediction tend to overlook specific feature characteristics, relying instead on deep neural networks or advanced attention mechanisms to learn meaningful feature interactions. In real-world scenarios, features can be categorized into groups based on prior information, which motivates the explicit consideration of interactions between groups of features. For example, the unique context of an item often has a substantial correlation with a particular user, and a specific item often has a strong relationship with a particular user demographic. An efficient model, therefore, requires an appropriate inductive bias to learn these relationships. To address this issue, we present a Context-aware Cross Feature Attentive Network (CCFAN) that explicitly considers the relationship or association between items and users. We categorize input variables into four groups: user, item, user context, and item context, which allows learning significant interactions between (user)-(item context) and (item)-(user context) in an explicit way. These interactions are learned using a multi-head self-attention network that includes modules for user-item interaction and cross-feature interaction. To demonstrate the effectiveness of CCFAN, we conduct experiments on two public benchmark datasets, MovieLens1M and Frappe, and one real-world dataset from an educational service provider, WJTB. The experimental results show that CCFAN not only outperforms previous state-of-the-art CTR methods but also offers a high degree of explainability.