Traditional recommendation methods, which typically focus on modeling a single user behavior (e.g., purchase), often face severe data sparsity issues. Multi-behavior recommendation methods offer a promising solution by leveraging user data from diverse behaviors. However, most existing approaches entangle multiple behavioral factors, learning holistic but imprecise representations that fail to capture specific user intents. To address this issue, we propose a multi-behavior method by modeling latent factors with an expert network (MBLFE). In our approach, we design a gating expert network, where the expert network models all latent factors within the entire recommendation scenario, with each expert specializing in a specific latent factor. The gating network dynamically selects the optimal combination of experts for each user, enabling a more accurate representation of user preferences. To ensure independence among experts and factor consistency of a particular expert, we incorporate self-supervised learning during the training process. Furthermore, we enrich embeddings with multi-behavior data to provide the expert network with more comprehensive collaborative information for factor extraction. Extensive experiments on three real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines, validating its effectiveness.

翻译：传统推荐方法通常聚焦于单一用户行为（如购买行为）建模，常面临严重的数据稀疏性问题。多行为推荐方法通过利用用户在不同行为下的数据提供了有效的解决方案。然而，现有方法多将多重行为因子纠缠学习，导致整体表征虽全面但不够精确，难以捕捉用户特定意图。为解决该问题，我们提出基于专家网络进行隐因子建模的多行为推荐方法（MBLFE）。本方法设计了一种门控专家网络，其中专家网络对整个推荐场景中的全部隐因子进行建模，每个专家专门负责学习特定隐因子；门控网络则为每位用户动态选择最优专家组合，实现用户偏好的更精准表征。为确保专家间的独立性与单一专家内部因子的一致性，我们在训练过程中引入自监督学习。此外，我们利用多行为数据丰富嵌入表征，为专家网络提供更全面的协同信息以提取因子。在三个真实数据集上的大量实验表明，本方法显著优于当前最优基线模型，验证了其有效性。