In recommender systems, multi-behavior methods have demonstrated significant effectiveness in addressing issues such as data sparsity—challenges commonly encountered by traditional single-behavior recommendation methods. These methods typically infer user preferences from various auxiliary behaviors and apply them to recommendations for the target behavior. However, existing methods face challenges in uncovering the interaction patterns for different behaviors from multi-behavior implicit feedback, as users exhibit varying preference strengths for different items across behaviors. To address this issue, this paper introduces a novel approach, Decision-Aware Preference Modeling (DAPM), for multi-behavior recommendation. We first construct a behavior-agnostic graph to learn comprehensive representations that are not affected by behavior factors, complementing the behavior-specific representations. Subsequently, we introduce an innovative contrastive learning paradigm that emphasizes inter-behavior consistency and intra-behavior uniformity to alleviate the “false repulsion” problem in traditional contrastive learning. Furthermore, we propose a multi-behavior hinge loss with boundary constraints to explicitly model users' decision boundaries across different behaviors, thereby enhancing the model’s ability to accurately capture users' inconsistent preference intensities. Extensive experiments on three real-world datasets demonstrate the consistent improvements achieved by DAPM over thirteen state-of-the-art baselines. We release our code at https://github.com/Breeze-del/DAPM.

Multi-behavior sequential recommendation (MBSR) aims to learn dynamic user preference from historical heterogeneous user interactions for identifying the next item under target behavior (i.e., purchase). Although significant efforts have been devoted to modeling users over observed multi-behavior interaction sequences, user modeling with dynamic behavior-aware multiple interests and elimination of inherent noises within these interactions are still underexplored. This limits user representations' awareness of true preference evolution and further constrains recommendation performance. To address the aforementioned issues, we propose a Multi-Interest Network with Simple Diffusion (MISD) via a combination of multi-interest learning and diffusion generative process for MBSR. Concretely, the dynamic multi-interest network is proposed to generate time-evolving personalized interests from the encoded dual-granularity user sequential patterns, leading to more accurate user preference learning. Additionally, simple diffusion is proposed to model the complex latent preference generation procedures in an iterative denoising manner, thereby alleviating the effect of noisy interactions. Extensive experiments on three real-world datasets demonstrate that MISD consistently outperforms various state-of-the-art recommendation methods under multiple settings (e.g., clean and noisy training).

Knowledge Graph(KG) contains structured attribute information which has been widely utilized for recommendations, as well as can effectively tackle the sparsity and cold start problems of collaborative filtering. In recent years, Graph Neural Networks (GNNs) serve as a novel deep learning technique that can significantly enhance recommendation performance. Unfortunately, existing KG-based GNN models are coarse-grained ignoring i)effective high-order feature interaction and fusion mechanism and ii)interpretable user latent intent decomposition. In this paper, we propose a new method named Knowledge Graph recommendation model via high-order feature Interaction and intent Decomposition(KGID), which explicitly models the fine-grained feature interaction and intent factors in KG-based GNN recommendation. Initially, high-order feature interactions are captured via the two-granularity convolutional neural networks on the item side. Next, the implicit intent factor behind the user decisions is modeled by two-level attention mechanisms. Ultimately, user representations and item representations are augmented simultaneously. We conduct experiments on three benchmark datasets to elucidate the superiority of the KGID to state-of-the-art baselines.