Sequential recommendation has become indispensable in modern digital services. Prevalent recommendation techniques formulate the recommendation task with a language instruction fed into large language models (LLMs) to generate recommendations. However, the implicit interaction scenario of recommendation task cannot provide explicit reasoning supervision to activate LLM's multi-step reasoning capability. Besides, the manner of reasoning for enhancing recommendation is still underexplored. Therefore, we investigate activating multi-step reasoning with users' interactions and propose a multi-step reasoning-enhanced LLM (MSR-Rec), which tightly integrates reasoning with recommendation from designing reasoning chain to reasoning-based recommendation. A task-decomposed reasoning chain is elaborately designed to imitate users' thinking process, seamlessly involving reasoning into recommendation. Following the reasoning chain, MSR-Rec synthesizes reasoning supervision and fine-tunes LLM to adapt for task-specific reasoning. In inference, bidirectional reasoning is implemented from user and item sides, performing a closed-loop reasoning for recommendation. Comprehensive experiments demonstrate that MSR-Rec achieves the state-of-the-art performance in both recommendation quality and reasoning interpretability, advancing the integration of reasoning and recommendation in LLM-based systems.

Beyond behavioral interaction records, multimedia recommendation scenarios possess abundant semantic signals, which provide excellent data support for user interest mining. Recently, the multimodal enhanced interaction graph has been actively explored and has achieved great progress. However, these methods overlook the capability disparity of various modalities in learning users' interests and lack the ability to explore the hierarchical relationships of interests in modality, resulting in suboptimal recommendation performance. Therefore, this work investigates intra-modality hierarchical learning and inter-modality guidance, proposing a hyperbolic self-distillation (HSD) model for multimedia recommendation. In each modality space, HSD introduces a hyperbolic propagation to filter users' hierarchical interests from the interaction graph effectively. Inter-modality interests are aligned further by a two-level self-distillation strategy to designate multimodal interactions to teach single-modal learning, aiming at teaching and learning to promote each other. Extensive experiments on four public datasets demonstrate that the proposed HSD outperforms leading baselines for multimedia recommendation, verifying the effectiveness of hierarchical propagation and two-level self-distillation in mining users' hierarchical interests.

To address the data sparsity issue, conventional graph-based models leverage structural signals from the interaction graph to embed users' interests. However, these models learn a uniform representation for interest modeling, which blends users' diverse intents and inevitably biases interest learning, hindering recommendations. Although the fine-grained paradigm can learn the intents of interactions separately to alleviate learning bias, the relationships among intents and the disentangled manner require elaborate design. Existing fine-grained models emphasize intent diversity and employ additional data splitting for disentanglement, which ignores the hierarchical relationship, exacerbates data sparsity, and increases the computational burden. To address these issues, we explore hierarchical intents and adaptive intent learning, proposing a hierarchical intent-based interest disentanglement (HIID) model for personalized recommendation. HIID introduces learnable intent queries to guide interest disentanglement from global interactions in a split-free manner. It raises a hierarchical intent hypothesis to involve hierarchical CF signals for interest modeling, where intents within the same level appear relatively diverse, and the in-depth intents are abstracted from the superficial ones. Both adaptive intent learning and hierarchical hypothesis help extract significant CF signals to promote personalized recommendation. Extensive experiments on public datasets show that the proposed HIID outperforms the state-of-the-art CF models for recommendation. Furthermore, HIID implements adaptive interest disentanglement in a split-free manner, improving the training efficiency of the recommender model compared to the existing fine-grained interest models.

The prevalent recommendation techniques explore the graph structure of interactions to alleviate the interaction sparsity issue for inferring users’ interests. These graph models focus on extracting local structural signals to model users’ interests, introducing grid-like distortion and ignoring the hierarchical tree-like structure when learning from the interaction graph. The learned interests lack significant hierarchical signals, resulting in suboptimal recommendation performance. In this article, we investigate geometric-augmented graph learning with hyperbolic and Euclidean geometries to delve into local structural and hierarchical knowledge from the interaction graph. A self-teaching network called geometric-augmented self-distillation (GASD) is proposed to transfer hierarchical knowledge from hyperbolic to Euclidean space. The transfer learning enables shrinking of the network into a primary student to implement effective and efficient inference in Euclidean space, preventing computational burden in hyperbolic space. Experiments on publicly available datasets demonstrate that the proposed GASD outperforms the state-of-the-art models, verifying the effectiveness and efficiency of knowledge transfer by self-distillation to aggregate knowledge adaptively for personalized recommendation.