Personalized sequential recommendation aims to predict the appropriate items to users from their behavioral sequences. To alleviate the data sparsity and interest drift issues, conventional approaches typically utilize the additional behaviors from other domains via cross-domain transition. However, existing cross-domain sequential recommendation (CDSR) algorithms follow the align-then-fusion paradigm which conducts the representation-level alignment across multiple domains and mechanically combine them for recommendation, overlooking the fine-grained multi-domain fusion. Inspired by the advancements of diffusion models (DMs) in distribution matching, we propose an align-for-fusion framework for CDSR to Harmonize triple preferences utilizing Dual-oriented DMs (HorizonRec). Specifically, we first investigate the uncertainty injection of DMs and attribute the fundamental factor of the instability in existing DMs recommenders to the stochastic noise and propose a Mixed-conditioned Distribution Retrieval strategy which leverages the retrieved distribution from users' authentic behavioral logic as a bridge across the triple domains, enabling consistent multi-domain preference modeling. To suppress the potential noise and emphasize target-relevant interests during multi-domain user representation fusion, we further propose a Dual-oriented Preference Diffusion method to guide the extraction of preferences aligned with users' authentic interests from each domain under the supervision of the mixed representation. We conduct extensive experiments and analyses on four CDSR datasets from two distinct platforms to verify the effectiveness and robustness of our HorizonRec and its effective mechanism in fine-grained fusion of triple domains. Our code and datasets are available in https://github.com/YongfuZha/HorizonRec.

Multimodal Entity Linking (MEL) aims to disambiguate mentions in multimodal contexts by grounding them to specific entities in a knowledge base. A pivotal challenge in MEL is capturing multi-level correspondences: the semantic consistency between mention-entity pairs and the complementary correlations across modalities. However, existing methods often suffer from element dominance due to their reliance on coupled interactions or coarse global aggregations. In response, we propose the Multi-Element Collaborative Interaction (MECI) framework. First, to capture multi-element mention-entity correspondences, we develop a Multi-view Experts Network that leverages a ''divide-and-conquer'' strategy for decoupled feature learning to mitigate element dominance, supported by a KL-guided routing mechanism that governs expert specialization and collaboration. Furthermore, to model cross-modal complementary correlations, we propose a Hierarchical Multimodal Interaction Module, where a dynamic modality-aware weighting network refines interactions across hierarchical semantic levels, thereby integrating multi-granular evidence to counteract element dominance. Finally, we incorporate a generative semantic refinement stage that utilizes large language models for zero-shot re-ranking. Extensive experiments on WikiDiverse, RichpediaMEL, and WikiMEL show that MECI consistently outperforms state-of-the-art baselines, improving Hits@1 by 1.95%, 7.30%, and 2.31%, respectively.

In recommender systems, providing reasonable explanations can enhance users’ comprehension of recommended results. Template-based explainable recommendation heavily relies on pre-defined templates, constraining the expressiveness of generated sentences and resulting in low-quality explanations. Recently, a novel approach was introduced, utilizing embedding representations of items and comments to address the issue of user IDs and item IDs not residing in the same semantic space as words, thus attributing linguistic meaning to IDs. However, these models often fail to fully exploit collaborative information within the data. In personalized recommendation and explanation processes, understanding the user’s emotional feedback and feature preferences is paramount. To address this, we propose a personalized explainable recommendation model based on self-attention collaboration. Initially, the model employs an attention network to amalgamate the user’s historical interaction feature preferences with their user ID information, while simultaneously integrating all feature information of the item with its item ID to enhance semantic ID representation. Subsequently, the model incorporates the user’s comment feature rhetoric and sentiment feedback to generate more personalized recommendation explanations utilizing a self-attention network. Experimental evaluations conducted on two datasets of varying scales demonstrate the superiority of our model over current state-of-the-art approaches, validating its effectiveness.