While previous works on Multi-Behavior Sequential Recommendation (MBSR) have made great efforts to exploit the behavior types, they often fail to fully leverage the behavior and attribute information to learn different aspects of user preferences. Firstly, for predicting user preferences under target behaviors, there is considerable room for improvement by capturing both intra- and inter-behavior item transition relationships at the sequence level, as well as extracting the inter-behavior common and different aspects of a user’s preferences at each timestep based on the contextual sequential information. Secondly, in MBSR, the effect of behavior types on item attributes has not been revealed, which is very important in real-world applications since a behavior such as view representing a user’s weak preferences could not explicitly indicate the user’s interests on an attribute. To address the above two issues, we propose a novel solution called Behavior-aware Attribute-infused Sequential Encoders (BASE) . Specifically, a module named tri-path preference learning is designed to capture user multi-aspect interests via a sequence encoder, a behavior encoder and an attribute encoder. Besides, a behavior-aware dual-granularity contrastive learning module constructs sample pairs in item and attribute granularities to enhance the model’s ability to utilize potential item and attribute information. Extensive empirical studies on three public datasets show that our BASE significantly outperforms various state-of-the-art methods from five different categories. The datasets and our implementation codes are released at https://github.com/Erin-Gr/BASE .

Sequential recommendation (SR) aims to capture users' dynamic preferences based on their historical interactions and provide personalized next-item prediction. Multi-behavior SR (MBSR) further considers behavior types of user-item interactions, which can reveal diverse user interests and alleviate the data sparsity issue w.r.t. the target purchase behaviors. Most existing MBSR approaches ignore the importance of target items closely related to user interests. Moreover, they often suffer from the problem of limited vector representation capability. To tackle the above two challenges, we propose a novel solution called target item-oriented conditional diffusion differential Transformer (ICDDT). Specifically, our ICDDT introduces distribution representations via the diffusion model, allowing effective utilization of target item information during training to better capture user preferences. Firstly, our ICDDT achieves a more appropriate behavior-aware step selection in the diffusion phase by distinguishing the sampling distributions of diffusion steps w.r.t. behavior types. Secondly, our ICDDT introduces three conditions of interaction sequences, target behaviors and diffusion steps into the reverse phase to guide the training of the differential Transformer-based approximator, generating denoised target item representations as user personalized interests. Finally, our ICDDT sets an inference step truncation factor to fit the diffusion step sampling distributions and accelerate the inference process. We conduct extensive experiments on two real-world datasets, where the results show that our ICDDT significantly outperforms all baselines on all metrics. The datasets, source codes and scripts are available at https://github.com/Erin-Gr/ICDDT.

Cross-domain sequential recommendation (CDSR) is proposed to alleviate the data sparsity issue while capturing users' sequential preferences. However, most existing methods do not explore the item transition patterns across different domains and can also not be applied to a multi-domain scenario.Moreover, previous methods rely on overlapping users as bridges to transfer knowledge, which struggles to capture the complex associations across domains without sufficient overlapping users. In this paper, we introduce item attributes into CDSR, and propose a heterogeneous graph transfer learning method to address these issues.Specifically, we construct a cross-domain heterogeneous graph to allow the association of user, item, and category nodes from different domains,and enhance the flexibility of the model by enabling message propagation between more nodes through edge expansion based on the semantic similarity and co-occurrence probability.In addition, we devise meta-paths from different perspectives for nodes at item, user and category levels to guide information aggregation, which can transfer knowledge across domains and reduce the reliance on the number of overlapping users.We further design attention modules to capture users' dynamic preferences from the item sequences they have interacted with in each domain, and explore the transition patterns within category sequences which reflect users' coarse-grained preferences.Finally, we perform knowledge transfer across different domains, and predict the most likely items that users will interact with in each domain. Extensive empirical studies on three real-world datasets indicate that our HGTL significantly outperforms the state-of-the-art baselines in all cases.