{"total":54,"items":[{"citing_arxiv_id":"2605.21987","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Generative Conversational Recommender System","primary_cat":"cs.IR","submitted_at":"2026-05-21T04:36:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A single autoregressive model for conversational recommendation that uses semantic item IDs, predicts response intent and target first, then generates the response, reporting up to 29% Recall@1 gains.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17994","ref_index":4,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search","primary_cat":"cs.IR","submitted_at":"2026-05-18T07:50:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GrowthGR combines ItemLTV counterfactual prediction with MultiGR generative retrieval and MoPO optimization to deliver 5.3% new item GMV lift and 0.3% overall GMV gain on Taobao production.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17779","ref_index":12,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Learning Variable-Length Tokenization for Generative Recommendation","primary_cat":"cs.LG","submitted_at":"2026-05-18T02:57:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"VarLenRec learns variable-length semantic IDs for generative recommendation by allocating longer codes to tail items via popularity-weighted information budget allocation, hyperbolic residual quantization, and a differentiable soft length controller.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17648","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation","primary_cat":"cs.AI","submitted_at":"2026-05-17T20:53:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SAPO computes per-reasoning-step group-relative advantages in RL to improve credit assignment for structured generation of semantic identifiers in recommendation systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14853","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Discrimination Is Generation: Unifying Ranking and Retrieval from a Tokenizer Perspective","primary_cat":"cs.IR","submitted_at":"2026-05-14T13:59:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DIG unifies ranking and retrieval by training the tokenizer jointly inside a ranking model, producing improved models for both from a single run.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14512","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Asymmetric Generative Recommendation via Multi-Expert Projection and Multi-Faceted Hierarchical Quantization","primary_cat":"cs.IR","submitted_at":"2026-05-14T07:55:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"AsymRec decouples input and output representations in generative recommendation via multi-expert semantic projection and multi-faceted hierarchical quantization, outperforming prior models by 15.8% on average.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14434","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL","primary_cat":"cs.IR","submitted_at":"2026-05-14T06:27:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"CQ-SID semantic IDs and EG-GRPO RL improve generative retrieval hit rates up to 26.76% over RQ-VAE baselines and deliver +1.15% GMV in live e-commerce A/B tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12617","ref_index":4,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"MLPs are Efficient Distilled Generative Recommenders","primary_cat":"cs.IR","submitted_at":"2026-05-12T18:05:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SID-MLP distills autoregressive generative recommenders into efficient position-specific MLP heads for Semantic ID tasks, achieving 8.74x faster inference with matching accuracy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11807","ref_index":33,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation","primary_cat":"cs.AI","submitted_at":"2026-05-12T09:01:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"AWARE augments generative next-POI recommendation with LLM agents that produce user-anchored narratives capturing events, culture, and trends, delivering up to 12.4% relative gains on three real datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11447","ref_index":8,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Conditional Memory Enhanced Item Representation for Generative Recommendation","primary_cat":"cs.IR","submitted_at":"2026-05-12T02:56:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"as SID, and the LLM directly generates the next item. Following the quantization-generation pipeline [7, 26, 29, 42, 44, 49], a line of research improve SID construction with content, collaborative signals, or task-aware tokenization [13, 27, 37, 38, 50], while oth- ers study SID-language alignment, long-SID generation, inductive decoding, and SID redistribution [8, 14, 40, 48]. While these works advance SID and generator design, ComeIR studies a less-explored representation bridge that reconstructs SID-token embeddings into item-aware inputs and restores them for token-level prediction. Conditional Memory.Memory-based modeling reuses recurring patterns through explicit or implicit storage. Classical 𝑛-gram lan-"},{"citing_arxiv_id":"2605.09040","ref_index":39,"ref_count":3,"confidence":0.98,"is_internal_anchor":true,"paper_title":"UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence","primary_cat":"cs.AI","submitted_at":"2026-05-09T16:26:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"UxSID models ultra-long user sequences with semantic-group shared interest memory using Semantic IDs and dual-level attention, achieving state-of-the-art performance and a 0.337% revenue lift in advertising A/B tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06331","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Expressiveness Limits of Autoregressive Semantic ID Generation in Generative Recommendation","primary_cat":"cs.IR","submitted_at":"2026-05-07T14:25:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Autoregressive semantic ID generation creates tree-induced probability correlations that prevent generative recommenders from capturing simple patterns; Latte adds latent tokens to relax these correlations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Liang, Yuqing Ding, Jing Chen, Chenyi Lei, Wenwu Ou, Han Li, and Kun Gai. Onesearch: A preliminary exploration of the unified end-to-end generative framework for e-commerce search. arXiv preprint arXiv:2509.03236, 2025. [4] Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. Recsys challenge 2018: automatic music playlist continuation. InRecSys, pages 527-528, 2018. [5] Jiaxin Deng, Shiyao Wang, Kuo Cai, Lejian Ren, Qigen Hu, Weifeng Ding, Qiang Luo, and Guorui Zhou. Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment.arXiv preprint arXiv:2502.18965, 2025. [6] Yijie Ding, Zitian Guo, Jiacheng Li, Letian Peng, Shuai Shao, Wei Shao, Xiaoqiang Luo, Luke Simon, Jingbo Shang, Julian McAuley, and Yupeng Hou."},{"citing_arxiv_id":"2605.05096","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation","primary_cat":"cs.IR","submitted_at":"2026-05-06T16:33:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CapsID uses probabilistic capsule routing and confidence-based termination to generate variable-length semantic IDs, improving recall by 9.6% over strong baselines with half the latency of dual-representation systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04726","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation","primary_cat":"cs.IR","submitted_at":"2026-05-06T10:20:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"RecGPT-Mobile runs a compact LLM on phones to understand evolving user intent from behaviors and improve mobile e-commerce recommendations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03397","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Revisiting General Map Search via Generative Point-of-Interest Retrieval","primary_cat":"cs.IR","submitted_at":"2026-05-05T06:19:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GenPOI is a generative POI retrieval system that unifies heterogeneous contexts via LLMs, uses geo-semantic tokenization, and applies proximity constraints to achieve superior performance on large-scale map search data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27747","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Position-Aware Drafting for Inference Acceleration in LLM-Based Generative List-Wise Recommendation","primary_cat":"cs.IR","submitted_at":"2026-04-30T11:37:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PAD-Rec augments standard draft models with item-position and step-position embeddings plus learnable gates, delivering up to 3.1x wall-clock speedup and 5% average gain over strong speculative-decoding baselines on four datasets while largely preserving recommendation quality.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26805","ref_index":12,"ref_count":2,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations","primary_cat":"cs.AI","submitted_at":"2026-04-29T15:35:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Bian Que is an agentic framework using a unified operational paradigm, flexible Skill Arrangement, and self-evolving mechanism to automate O&M tasks, achieving 75% alert reduction and over 50% MTTR cut in production deployment.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[10] Jiaxin Deng, Shiyao Wang, Kuo Cai, Lejian Ren, Qigen Hu, Weifeng Ding, Qiang Luo, and Guorui Zhou. Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment.arXiv preprint arXiv:2502.18965, 2025. [11] Paolo Notaro, Jorge Cardoso, and Michael Gerndt. A survey of aiops methods for failure management.ACM Transactions on Intelligent Systems and Technology (TIST), 12(6):1-45, 2021. [12] Yinfang Chen, Jiaqi Pan, Jackson Clark, Yiming Su, Noah Zheutlin, Bhavya Bhavya, Rohan Arora, Yu Deng, Saurabh Jha, and Tianyin Xu. Stratus: A multi-agent system for autonomous reliability engineering of modern clouds.arXiv preprint arXiv:2506.02009, 2025. [13] Evelien Riddell, James Riddell, Gengyi Sun, Micha'L Antkiewicz, and Krzysztof Czarnecki."},{"citing_arxiv_id":"2604.25787","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation","primary_cat":"cs.IR","submitted_at":"2026-04-28T15:56:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"RecoChain unifies generative candidate generation via hierarchical semantic IDs and SIM-based ranking in a single Transformer to improve top-K recommendation performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25291","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space","primary_cat":"cs.IR","submitted_at":"2026-04-28T06:57:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GloRank reformulates list-wise reranking as token generation over a global item identifier space, using supervised pre-training followed by reinforcement learning to maximize list-wise utility and outperforming baselines on benchmarks and industrial data.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Consequently, the reranking task is refor- mulated fromselecting dynamic local indicestogenerating static global identifiers. This not only ensures that the model learns a stable evaluation standard that directly correlates with item semantics, but also is beneficial for improving cross-stage modeling consistency since SIDs are widely adopted in retrieval stages in many modern designs [9, 35]. To ensure training stability and maximize perfor- mance, we introduce a two-stage optimization pipeline (inspired by the modern training framework of large recommendation mod- els [45, 50]) adapted to the reranking task. A supervised pre-training phase first initializes the model by imitating high-quality reference lists, and a reinforcement learning-based post-training phase then"},{"citing_arxiv_id":"2604.24472","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Modeling Behavioral Intensity and Transitions for Generative Recommendation","primary_cat":"cs.IR","submitted_at":"2026-04-27T13:40:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BITRec improves generative multi-behavior recommendation by modeling behavioral intensity via separated pathways and transitions via learnable relation matrices, reporting 15-23% gains on large retail datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.23568","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Green-Red Watermarking for Recommender Systems","primary_cat":"cs.IR","submitted_at":"2026-04-26T07:16:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"GREW uses a secret-key-driven green-red item partition and three ranking-integrated modules to embed verifiable watermarks in recommender systems that resist extraction attacks without data injection.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"extraction attacks compared to the existing baseline? • RQ4: How do the hyperparameters influence the trade-off be- tween watermark validity and model utility? •RQ5: What are the contributions of each core module? 5.1 Experimental Setup 5.1.1 Datasets.To evaluate the performance of the proposed GREW framework, we conducted experiments on three recommendation datasets: MovieLens-1M (ML-1M) [6], Amazon Beauty [ 19], and Steam Review [10]. Detailed statistics for these datasets are pre- sented in Appendix A. Following established evaluation protocols [10, 24], the leave-one-out strategy is employed for training. 5.1.2 Base Models.We evaluate our framework using three repre- sentative sequential recommendation models: • NARM[ 13] is an RNN-based recommender that combines an"},{"citing_arxiv_id":"2604.23156","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation","primary_cat":"cs.IR","submitted_at":"2026-04-25T06:05:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Pro-GEO introduces a geo-centroid coordinate system and geo-rotary position encoding to model geographic proximity as rotational transformations, enabling balanced semantic-spatial modeling in local service recommendations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22504","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders","primary_cat":"cs.IR","submitted_at":"2026-04-24T12:31:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Beam-search negatives induce partial AUC optimization in GRPO for LLM recommenders; Windowed Partial AUC and TAWin improve Top-K alignment on four datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22881","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches","primary_cat":"cs.LG","submitted_at":"2026-04-24T03:44:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MTServe achieves up to 3.1x speedup for generative recommendation model serving by using hierarchical caches with host RAM and system optimizations while keeping cache hit ratios above 98.5%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22180","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","primary_cat":"cs.IR","submitted_at":"2026-04-24T03:11:51+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ResRank unifies retrieval and listwise reranking by compressing passages to one token each, using residual connections and cosine-similarity scoring, achieving competitive effectiveness on TREC DL and BEIR benchmarks with zero generated tokens.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18375","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters","primary_cat":"cs.CL","submitted_at":"2026-04-20T15:02:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"IceBreaker applies resonance-aware interest distillation and interaction-oriented starter generation with preference alignment to create cold-start conversation openers, yielding +0.184% active days and +9.425% CTR gains in production A/B tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15739","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"On the Equivalence Between Auto-Regressive Next Token Prediction and Full-Item-Vocabulary Maximum Likelihood Estimation in Generative Recommendation--A Short Note","primary_cat":"cs.IR","submitted_at":"2026-04-17T06:27:42+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Auto-regressive next-token prediction is strictly equivalent to full-vocabulary maximum likelihood estimation in generative recommendation under bijective item-to-token-sequence mapping.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13801","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"DUET: Joint Exploration of User Item Profiles in Recommendation System","primary_cat":"cs.IR","submitted_at":"2026-04-15T12:37:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DUET uses a three-stage joint profile generator with RL feedback to create consistent user-item textual profiles that outperform independent generation in recommendation tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13468","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines","primary_cat":"cs.IR","submitted_at":"2026-04-15T04:44:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AuthGR is the first generative retriever to explicitly incorporate document authority alongside relevance using multimodal scoring and progressive training, yielding efficiency gains and real-world engagement improvements.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13273","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Mitigating Collaborative Semantic ID Staleness in Generative Retrieval","primary_cat":"cs.IR","submitted_at":"2026-04-14T20:06:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A model-agnostic SID alignment update mitigates staleness from temporal drift in user-item interactions for generative retrievers, improving Recall@K and nDCG@K while reducing compute by 8-9x versus full retraining.","context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"model generates from the user context [18]. A key design choice is how to construct SIDs. Content-only SIDs (derived from metadata or multimodal features) are stable and effec- tive in cold-start scenarios [20], but can miss behavioral structure captured in interaction logs [26]. Recent systems therefore build interaction-informed SIDs, often improving retrieval quality [5, 7]. In practical settings, collaborative patterns are non-stationary: user interests, item popularity, and logging policies evolve over time, motivating periodic SID refreshes to capture the latest collab- orative structure. Yet refreshing SIDs introduces a challenge: new token assignments may become incompatible with the retriever's previously learned output space."},{"citing_arxiv_id":"2604.12234","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"UniRec: Bridging the Expressive Gap between Generative and Discriminative Recommendation via Chain-of-Attribute","primary_cat":"cs.IR","submitted_at":"2026-04-14T03:13:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"UniRec bridges the expressive gap in generative recommendation by prefixing semantic ID sequences with structured attribute tokens, recovering explicit feature crossing and yielding +22.6% HR@50 gains plus online lifts in PVCTR, orders, and GMV.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10471","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"SID-Coord: Coordinating Semantic IDs for ID-based Ranking in Short-Video Search","primary_cat":"cs.IR","submitted_at":"2026-04-12T05:51:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SID-Coord coordinates semantic IDs with hashed item IDs via attention fusion, adaptive gating, and interest alignment, yielding +0.664% long-play rate and +0.369% playback duration gains in production search ranking.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.08933","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems","primary_cat":"cs.IR","submitted_at":"2026-04-10T04:02:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"IAT compresses each historical interaction instance into a unified embedding token via temporal-order or user-order schemes, allowing standard sequence models to learn long-range preferences with better performance and transferability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05113","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation","primary_cat":"cs.IR","submitted_at":"2026-04-06T19:18:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CRAB mitigates popularity bias in generative recommenders by rebalancing the semantic token codebook through splitting popular tokens and applying a tree-structured regularizer to boost representations for unpopular items.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02684","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"MBGR: Multi-Business Prediction for Generative Recommendation at Meituan","primary_cat":"cs.IR","submitted_at":"2026-04-03T03:26:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MBGR is a new generative recommendation framework using business-aware semantic IDs, multi-business prediction, and label dynamic routing to handle multiple businesses without seesaw effects or representation confusion, validated by experiments and deployed at Meituan.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Wide & deep learning for recommender systems. InProceedings of the 1st workshop on deep learning for recommender systems. 7-10. [4] Jiaxin Deng, Shiyao Wang, Kuo Cai, Lejian Ren, Qigen Hu, Weifeng Ding, Qiang Luo, and Guorui Zhou. 2025. Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment.arXiv preprint arXiv:2502.18965 (2025). [5] Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction.arXiv preprint arXiv:1703.04247(2017). [6] Ruidong Han, Bin Yin, Shangyu Chen, He Jiang, Fei Jiang, Xiang Li, Chi Ma, Mincong Huang, Xiaoguang Li, Chunzhen Jing, et al. 2025. MTGR: Industrial- Scale Generative Recommendation Framework in Meituan."},{"citing_arxiv_id":"2605.02902","ref_index":8,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration","primary_cat":"cs.HC","submitted_at":"2026-03-30T14:06:54+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Red-Rec uses AI-initiated summaries and low-effort option selection to help users with vague intent explore more broadly and with higher serendipity than user-initiated chat while requiring less typing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.24422","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework","primary_cat":"cs.IR","submitted_at":"2026-03-25T15:33:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"OneSearch-V2 improves generative retrieval via latent reasoning and self-distillation, achieving +3.98% item CTR, +2.07% buyer volume, and +2.11% order volume in online A/B tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.24226","ref_index":7,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking","primary_cat":"cs.IR","submitted_at":"2026-03-25T12:00:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"UniScale couples entire-space data construction with a hierarchical fusion transformer to improve scaling behavior and deliver 1.70% purchase and 2.04% GMV lifts in large-scale e-commerce search A/B tests.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.14259","ref_index":4,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items","primary_cat":"cs.IR","submitted_at":"2026-03-15T07:31:28+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"GenRecEdit injects cold-start items into generative recommendation models via context-aware token editing and interference-reducing triggers, boosting cold-start accuracy while using only 9.5% of retraining time.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.23978","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer","primary_cat":"cs.IR","submitted_at":"2026-02-27T12:39:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.23964","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"RAD-DPO: Robust Adaptive Denoising Direct Preference Optimization for Generative Retrieval in E-commerce","primary_cat":"cs.IR","submitted_at":"2026-02-27T12:17:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"RAD-DPO adds token-level gradient detachment, similarity-based dynamic reward weighting, and a multi-label global contrastive objective to DPO for better handling of hierarchical Semantic IDs and noisy feedback in e-commerce generative retrieval.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.22913","ref_index":2,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress","primary_cat":"cs.IR","submitted_at":"2026-02-26T12:00:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SIGMA deploys a semantic-grounded, instruction-driven generative model with hybrid tokenization and adaptive fusion for multi-task recommendation at AliExpress.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.10455","ref_index":10,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Compute Only Once: UG-Separation for Efficient Large Recommendation Models","primary_cat":"cs.IR","submitted_at":"2026-02-11T02:53:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"UG-Separation framework disentangles user-side and item-side flows in TokenMixer dense-interaction models to enable reusable user computations, cutting inference latency up to 20% in ByteDance production scenarios.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.03324","ref_index":15,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation","primary_cat":"cs.IR","submitted_at":"2026-02-03T09:51:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SCASRec unifies ranking and redundancy elimination for route lists via stepwise corrective rewards and an adaptive end-of-recommendation token, claiming SOTA results on two datasets and real deployment.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.18664","ref_index":9,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"S$^2$GR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendation","primary_cat":"cs.IR","submitted_at":"2026-01-26T16:40:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"S²GR adds stepwise thinking tokens with contrastive supervision on codebook clusters to balance computational focus and ground reasoning paths in generative recommendation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.27157","ref_index":24,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Survey on Generative Recommendation: Data, Model, and Tasks","primary_cat":"cs.IR","submitted_at":"2025-10-31T04:02:58+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"The Scaling Law of LRMs: HSTU [216], Meta ROO [41], RankMixer [243], KuaiFormer [98], LEARN [62], HLLM [9],SRP4CTR [44], MTGR [45]; End-to-End Recommendations: OneRec [23], OneRec-V2 [240] OneRec-Think [112],UniROM [135], OneSug [43], EGA-V2 [238], URM [63] Diffusion-BasedGenerativeRecommendation (Sec4.3) Enhancing traditional recommenders: ADRec [11], ARD-SR [156], DGFedRS [24], DDRM [230], DiffuASR [88],InDiRec [139], RecFlow [61], DRGO [229], DMCDR [83], MoDiCF [77]; Diffusion as recommenders: DiffRec [174],DreamRec [205], DiffRIS [129], PreferDiff [108], TDM [121], DiffDiv [7], DDSR [192], DiQDiff [119]; PersonalizedContent Generation: DreamVTON [193], InstantBooth [148], OOTDiffusion [197] Task &Application-LevelOpportunities(Sec 5)"},{"citing_arxiv_id":"2510.21242","ref_index":8,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation","primary_cat":"cs.IR","submitted_at":"2025-10-24T08:25:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BLOGER is a bi-level optimization framework that jointly optimizes the tokenizer and recommender for generative recommendation, outperforming prior methods on real-world datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.11317","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Next Interest Flow: A Generative Pre-training Paradigm for Recommender Systems by Modeling All-domain Movelines","primary_cat":"cs.IR","submitted_at":"2025-10-13T12:13:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Next Interest Flow models user intent as continuous evolutionary trajectories on a high-dimensional latent interest manifold with kinematic constraints, bidirectional alignment, and temporal causality mechanisms, yielding reported gains on industrial CTR data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.18736","ref_index":4,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Denoising Neural Reranker for Recommender Systems","primary_cat":"cs.IR","submitted_at":"2025-09-23T07:29:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DNR is an adversarial denoising neural reranker that extends score error minimization with three objectives to denoise retriever scores and align them with user feedback in two-stage recommender systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.13648","ref_index":13,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Sequential Data Augmentation for Generative Recommendation","primary_cat":"cs.LG","submitted_at":"2025-09-17T02:53:25+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GenPAS unifies common data augmentation strategies for generative recommendation as special cases of a bias-controlled stochastic sampling process and demonstrates gains in accuracy, data efficiency, and parameter efficiency on benchmarks and industrial data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}