# Multi-Feature Behavior Relationship for Multi-Behavior Recommendation
## 1 Introduction  
Personalized recommendation systems are commonly utilized in many internet services, e.g., e-commerce and advertising systems [1]. They can efficiently handle the information overload problem. The fundamental goal is to fully reflect user preferences for items using data from user interactions. As shown in Figure 1, in practical recommendation scenarios, user interaction behaviors such as page view, add-to-cart, purchase, and add-to-favorites are often diverse. Each behavior reveals different contextual features, realizing multiple interaction modes between users and items. Multi-behavior recommendation is a comprehensive consideration of different behaviors to obtain a more comprehensive understanding of user preferences. Many scholars have already explored and validated multi-behavior recommendations in practice [2, 3, 4]. In the early stage, [PERSON] et al. [5] used matrix decomposition techniques to mine users' preferences from multiple types of interaction data. Recently, many researchers have proposed multi-behavior recommendation models based on graph neural networks to capture higher-order interaction information. For example, GNMR [6] is based on a graph messaging architecture shown to model user interaction heterogeneity. MBGCN [7] designs a unified graph structure to represent multi-behavior interactions. In addition, researchers have modeled sequential information in multi-behavior recommendations; for instance, MRIG [1] creates multi-relational behavior graphs for session-based recommendations. ATRank [8] models feature interactions between different behaviors using a self-attentive mechanism. Although the above models have performed well in modeling multi-behavior interaction features, the following challenges currently exist in modeling multi-behavior relationships:  
**Modeling with multi-feature behavior. ** Many current approaches utilize sequence-based or graph-based models to construct user interest representations, which cannotsimultaneously model users' preferences from multiple feature perspectives [9]. Potential item correlations within the sequence of user behavior interactions can reflect users' behavior habits and interest evolution patterns. In addition, the user-item interaction graph composed of all user behaviors can reflect the overall interests of users. Therefore, complementing the above two modeling approaches and mining interaction information from multiple perspectives can more accurately represent user preferences. **Correlation between learning behaviors. ** A user may click on an item link or add a favorite item to the shopping cart, and the user's interest in the item is reflected in these activities [1]. In addition, non-purchasing behaviors can reveal a user's underlying interest, particularly with respect to brand-new items that the user has not purchased before. Learning the correlations between different behaviors can more accurately uncover a user's potential interest and reduce the noise generated by accidental item purchases. To address the above challenges, we present a framework for multi-behavior recommendation based on user multi-feature behavior relationships that jointly mines user preferences from multi-featured sequences and multi-featured heterogeneous graphs. Specifically, we first design a feature encoding layer which jointly models users' multi-feature behaviors through sequence encoder and graph encoder to model users' multi-feature interaction information. Then, we use an attention mechanism in the behavior relation encoder layer to learn the relationship between two behaviors in order to characterize the correlation between different behaviors. A fully connected neural network recognizes the significance of distinct behaviors and assigns weights to each one. The following are this paper's main contributions:  
* This work focuses on capturing multi-feature behavior interaction patterns of users from their interaction behavior sequences and user-item interaction graphs for multi-behavior recommendation.