Cross-domain sequential recommendation aims to generate accurate recommendations by leveraging users' historical interactions across domains. However, existing methods have two limitations: 1) When transferring user's preferences from the source domain, they encode preferences into a static and holistic representation, ignoring the rich information inherent in the dynamic evolution of user preferences over time; 2) They adopt a distribution-agnostic full-transfer strategy, failing to effectively limit the transfer degree of source-domain preferences according to different data distributions, which poses a risk of negative transfer. To address these issues, we propose the Dynamic Preference Transfer (DPT) model. Unlike existing methods, DPT places greater emphasis on the dynamic transfer of real-time preferences. First, DPT captures the causal features through the causal self-attention mechanism, and then realizes dynamic preference transfer at each time step via the causal cross-attention mechanism, thereby tracking the temporal dynamics of preferences from source domains. Second, to mitigate the negative transfer issue, a temperature-controlled mechanism is designed to adaptively balance source and target domain preferences, leveraging a temperature-controlled sigmoid function to effectively suppress interference from irrelevant preferences. Experimental results on multiple benchmark datasets show that the proposed method achieves significant performance improvements compared with the state-of-the-art (SOTA) methods, verifying its effectiveness and superiority. The codes are available in https://github.com/iryand/DPT.

Personalized news recommendations are challenging due to the huge number of daily articles. While deep learning has achieved success in news recommendations, methods in the past often overlook the diversity of users' preferences. Recent works have explored multi-interest models to address this limitation. However, interests have different effects on click behavior, and directly modeling the matching between interests and candidates leads to the issue of spurious correlations. Specifically, when highly correlated interests obscure the true motivation for clicking, the model is unable to distinguish the interest that actually caused the click. To address this problem, this paper re-models the relationship between interests and click behavior from a causal perspective. Our proposed Counterfactual Weighted method for user M ulti-Interest modeling (CWMI) consists of a disentangled multi-interest extractor and an interest re-weighting module. Specifically, we first model the effect of interest on click behavior from a causal perspective. Then, we learn the disentangled user interests that only incorporate information from the currently clustered news. Finally, in the counterfactual world, we intervene with the current interest and re-weight it by comparing the changes in the ranking of candidates. We learned about the evolution of interest over time additionally. Experimental results on real-world news datasets demonstrate the effectiveness of the proposed methods, including disentangling interests and identifying the real interest that motivates clicks.

Multi-behavior sequential recommendation (MBSR) predicts a user's next item of interest based on their interaction history across different behavior types. Although existing studies have proposed capturing the correlation between different types of behavior, two important challenges have not been explored: i) Dealing with heterogeneous item transitions (both global and local perspectives). ii) Mitigating the issue of noise that arises from the incorporation of auxiliary behaviors. To address these issues, we propose a novel solution, Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation (GHTID). In particular, we view the transitions between behavior types of items as different relationships and propose two heterogeneous graphs. By considering the relationship between items under different behavioral types of transformations, we propose two heterogeneous graph convolution modules and explicitly learn heterogeneous item transitions. Moreover, we utilize two attention networks to integrate long-term and short-term interests associated with the target behavior to alleviate the noisy interference of auxiliary behaviors. Extensive experiments on four real-world datasets demonstrate that our method outperforms other state-of-the-art methods.

The task of sequential recommendation aims to predict a user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s behaviours and dynamic characteristics, while often ignoring high-order collaborative connections when modelling user preferences. Some recent works try to use graph-based methods to introduce high-order collaborative signals for sequential recommendation. However, these methods are flawed by two problems: the sequential patterns cannot be effectively mined and their way of introducing high-order collaborative signals is not suitable for sequential recommendation. To address these problems, we propose to fully exploit sequence features and model high-order collaborative signals for sequential recommendation. We propose a N eighbor user I ntention-based S equential Rec ommender (NISRec), which utilizes the intentions of high-order connected neighbor users as high-order collaborative signals in order to improve recommendation performance for the target user. The NISRec contains two main modules: the neighbor user intention embedding module (NIE) and the fusion module. The NIE module describes both the long-term and short-term intentions of neighbor users and aggregates them separately. The fusion module uses these two types of aggregated intentions to model high-order collaborative signals in both the embedding process and user preference modelling phase for recommendations of the target user. Experimental results show that our new approach outperforms the state-of-the-art methods on both sparse and dense datasets. Extensive studies further show the effectiveness of the diverse neighbor intentions introduced by the NISRec.

Sequential recommendation aims to capture users' dynamic preferences, in which data sparsity is a key problem. Most contrastive learning models leverage data augmentation to address this problem, but they amplify noises in original sequences. Contrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb the user's main intention, which results in the dissimilarity of two views.