Generative recommendation represents items as semantic identifiers (SIDs), i.e., sequences of discrete tokens, and typically predict the next item autoregressively. Despite their strong performance, such methods suffer from two limitations: (1) unidirectional constraints, where causal attention enforces left-to-right dependencies and limits global semantic modeling; and (2) error accumulation, where early mistakes propagate to later tokens and hurt recommendation accuracy. To address these issues, we propose LLaDA-Rec, a discrete diffusion framework that reformulates next-item recommendation as parallel SID generation. By combining bidirectional attention with an adaptive generation order, LLaDA-Rec captures richer inter-item and intra-item dependencies while reducing error accumulation. Our framework includes three key components: (1) a parallel tokenization mechanism for diffusion-compatible SIDs; (2) masking strategies at both the user-history and next-item levels; and (3) an adapted beam search algorithm for adaptive-order discrete diffusion decoding. Experiments on three public datasets show that LLaDA-Rec consistently outperforms both conventional ID-based recommenders and strong generative baselines, highlighting discrete diffusion as a promising paradigm for generative recommendation.

Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for personalization. To address this, we propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. Based on this, we introduce VAPS, a value-aware personalized search model that selectively incorporates high-value consultations through a consultation–user action interaction module and an explicit objective that aligns consultations with user actions. Experiments on both public and commercial datasets show that VAPS consistently outperforms baselines in both retrieval and ranking tasks. Codes are available at https://github.com/E-qin/VAPS.