Recent studies indicate a fundamental incompatibility between ID representations and language model (LM) representations, as they capture behavioral and semantic spaces respectively. This mismatch leads LM representations to consistently underperform ID representations in recommendation tasks. In this work, we revisit this problem and show, from an information-theoretic perspective, that LLM representations retain all discriminative information in ID representations. Based on this, we introduce a Profile-then-Embedding (PtE) framework for recommendation, consisting of a Profile Stage, in which semantic user and item profiles are generated jointly through LLM-based bidirectional reasoning over user-item interactions, and a Personalized Embedding Stage, which encodes these profiles into task-aligned recommendation embeddings. We demonstrate PtE’s effectiveness across three benchmark datasets, including cold-start and long-tail scenarios, achieving substantial gains in both discriminative and generative recommendation models.

Lifelong knowledge editing (LKE) aims to incrementally correct factual inaccuracies in large language models (LLMs), but sequential edits can lead to substantial degradation of capabilities. Existing approaches primarily rely on static parameter regularization, which restricts knowledge integration and fails to prevent cumulative capability degradation. We argue that an important source of this degradation lies in the temporal mismatch between locally editable factual knowledge and procedural knowledge, which is gradually acquired, guides task execution, and cannot be reliably updated by rapid edits. To this end, we formulate LKE as a dual-timescale process, explicitly decoupling fast-updating factual knowledge from slow-evolving procedural knowledge. Based on this formulation, we propose CaPEdit, a framework that preserves model capabilities under LKE. It first synthesizes procedural knowledge across successive edits, and subsequently performs parameter updates guided jointly by factual supervision and the synthesized procedural signal. To ensure stability under long edit sequences, CaPEdit is trained via a hybrid optimization scheme, combining step-wise updates for rapid factual correction with trajectory-level optimization to facilitate gradual procedural adaptation. Experiments demonstrate that CaPEdit improves capability preservation across all fundamental capabilities by 49.78%, achieves superior editing performance, and requires only 18.07% of the editing time of most existing methods.

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a specific aspect within a given sentence. Most existing methods predominantly leverage semantic or syntactic information based on attention scores, which are susceptible to interference caused by irrelevant contexts and often lack sentiment knowledge at a data-specific level. In this paper, we propose a novel Dynamic Multi-granularity Attribution Network (DMAN) from the perspective of attribution. Initially, we leverage Integrated Gradients to dynamically extract attribution scores for each token, which contain underlying reasoning knowledge for sentiment analysis. Subsequently, we aggregate attribution representations from multiple semantic granularities in natural language, enhancing a profound understanding of the semantics. Finally, we integrate attribution scores with syntactic information to capture the relationships between aspects and their relevant contexts more accurately during the sentence understanding process. Extensive experiments on five benchmark datasets demonstrate the effectiveness of our proposed method.

The emergence of personalized generation has made it possible to create texts or images that meet the unique needs of users. Recent advances mainly focus on style or scene transfer based on given keywords. However, in e-commerce and recommender systems, it is almost an untouched area to explore user historical interactions, automatically mine user interests with semantic associations, and create item representations that closely align with user individual interests.In this paper, we propose a brand new framework called **I**nterest-**A**ugmented **M**ultimodal **G**enerator (**I-AM-G**). The framework first extracts tags from the multimodal information of items that the user has interacted with, and the most frequently occurred ones are extracted to rewrite the text description of the item. Then, the framework uses a decoupled text-to-text and image-to-image retriever to search for the top-K similar item text and image embeddings from the item pool. Finally, the Attention module for user interests fuses the retrieved information in a cross-modal manner and further guides the personalized generation process in collaboration with the rewritten text.We conducted extensive and comprehensive experiments to demonstrate that our framework can effectively generate results aligned with user preferences, which potentially provides a new paradigm of **Rewrite and Retrieve** for personalized generation.