Quotient DAGs enable forward-flow importance sampling and exact unordered slate propensities via Forward-DP for autoregressive slate loggers under set-sufficient interfaces.
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Self-attentive sequential recommendation
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2026 20representative citing papers
OSA improves LLM-based recommenders by anchoring ordinal preference levels as numeric tokens in the model's latent space to retain fine-grained strength information when fusing collaborative signals.
SkillGraph builds a reusable execution-transition graph prior from LLM trajectories and applies it via hybrid retrieval plus learned reranking to raise tool-sequence quality on ToolBench and API-Bank benchmarks.
Gryphon unifies Semantic-ID generation with direct item-level scoring in a single encoder-decoder pass, attaining higher Recall@1000 than vanilla and collision-resolved generative retrieval baselines on an industrial music dataset while simplifying the candidate pipeline in a live A/B test.
DREAM proposes intent-aware tokenization, frozen-model evaluation, and dynamic beams to refine early SID assignments and improve cold-start performance in generative recommenders on Amazon benchmarks.
idSCD uses semantic correlation descriptors to perform dataset membership inference by comparing learned semantic structures, outperforming baselines in NLI, emotion, and medical text experiments.
A Llama-based model trained on serialized user stories unifies item, carousel, and search ranking and outperforms specialist baselines offline while improving some online metrics and reducing latency.
FLUID introduces LUCID semantic codes from a multimodal encoder to retire item IDs in livestreaming rankers, with staged warmup yielding online gains of +0.55% watch duration and +2.05% cold-start views.
VISOR is a VLM-based automated test oracle that evaluates robot task correctness and quality from videos while reporting its own uncertainty, tested on GPT and Gemini across four tasks and over 1000 videos with Gemini showing higher recall and GPT higher precision but low uncertainty-correctness tie
LASAR uses two-stage supervised training plus reinforcement learning to ground semantic IDs, align latent reasoning trajectories to CoT hidden states via KL divergence, and adaptively choose reasoning depth, halving average steps while improving quality on three datasets.
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.
A persona-driven SBRS framework learns unsupervised user personas from an LLM-initialized heterogeneous KG and incorporates them into data-driven sequential recommenders, reporting consistent gains over session-history baselines on Amazon Books and Movies & TV.
BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
A late-fusion model of CF, RL bandit, and TOPSIS achieves NDCG@5=0.3040 on JobHop (outperforming baselines) but remains competitive without significant gains on Karrierewege, with the bandit branch deactivating in persistence-dominated data.
HSUGA improves LLM-enhanced sequential recommendation via staged hierarchical semantic understanding for better preference extraction and group-aware alignment that varies intensity by user activity level.
TraXion supplies a unified pre-training approach for multi-entity spatiotemporal event streams that outperforms task-specific baselines on mobility tasks and transfers unchanged to authentication logs and ICU mortality prediction.
PHKT uses personalized dynamic hypergraphs and KAN-Transformer to outperform baselines in multi-behavior sequential recommendation on Tmall, RetailRocket, and IJCAI.
SAILRec uses dual-side semantic alignment and hierarchical attention steering to improve how LLMs incorporate collaborative embeddings for recommendations, outperforming baselines on MovieLens-1M and Amazon-Book datasets.
A survey that categorizes deep learning models for point cloud tasks by backbone architecture, evaluates benchmark performance, and outlines challenges and future research directions.
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