Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 3847-3858· 0 citations· 18 references
Abstract
Recommendation systems are indispensable for helping users navigate the vast product catalogs of modern online platforms. Recently, generative recommendation has emerged as a promising paradigm due to its unified and flexible formulation, and it has been adopted in various commercial scenarios. However, we find that different tokenization algorithms share a common issue: semantic branch drift, where semantically similar items are often assigned to different branches of the semantic tree. This phenomenon leads to severe information loss in the generated semantic IDs and significantly degrades recommendation accuracy. To address this problem, we propose HiST (Hierarchical Semantic Tree Augmentation), a novel framework that leverages branch information to bridge the semantic gap. The core innovation of HiST lies in using a trained tokenizer to construct a semantic tree over all items and extracting hierarchical information through a bottom-up approach. This hierarchical information is then fused with the original semantic IDs to enhance representation capacity. During end-to-end training, HiST improves recommendation performance by introducing separate prediction objectives for semantic IDs and hierarchical description components, enabling the model to effectively learn both discrete structure and semantic relationships. Experimental results demonstrate that HiST significantly improves recommendation performance across multiple datasets. Furthermore, experiments verify that HiST can be effectively integrated with various existing tokenization methods to enhance recommendation model performance, achieving consistent improvements across evaluated settings.
ReSOT is proposed, a unified framework that Re-balances Semantic ID learning via Optimal Transport for GR and provides a principled tokenization scheme that preserves relational structure while assigning codes in a collision-aware and semantics-consistent manner.
Renwu Geng, Yi-Ming Xu, Fengxin Li et al.· Proceedings of the 32nd ACM...· 0 citations
BARGE is proposed, which employs Item Context-Aware Attention (ICA) to restore item-level structure during encoding, and Hierarchical Path Reranking (HPR) together with Dual-Path Decoding (DPD) to suppress semantic drift from two complementary angles during decoding.
The Hierarchical Semantic Interest Evolution Network (HSIEN), a novel generative-discriminative framework that significantly alleviates modality misalignment and enhances CTR prediction performance through feature complementarity, is proposed.
Yi-Fan Cao, Rui Wu, Xiang Wang et al.· Proceedings of the Thirty-Fi...· 0 citations
Industrial recommender systems typically rely on cascaded retrieval, pre-ranking, ranking, and reranking stages, whose separately optimized models limit scaling, fragment decision making, and lack semantic knowledge and reasoning. We present TGR (Tencent Generative Recommendation), an industrial framework that advances recommendation toward the generative paradigm along three coupled directions. TGR-GenRank upgrades ranking through CCFormer, which combines unified feature tokenization, a scalable Transformer backbone, feature-field separated cross attention, subspace token mixing, and hierarchical sequence compression while retaining per-item multi-task outputs. TGR-GenRec explores end-to-end generation under two paradigms: BARGE bridges item-boundary loss and semantic drift in hierarchical semantic-ID generation through item context-aware attention, hierarchical path reranking, and orthogonal dual-path decoding; HiGR performs whole-slate generation with prefix-structured semantic IDs, coarse-to-fine decoding, and listwise multi-objective alignment. TGR-Reason injects offline-generated semantic-ID reason tokens into online decoding, providing reasoning without request-time rollout. TGR is deployed across Tencent production surfaces serving hundreds of millions of users. CCFormer delivers significant gains in five A/B-tested scenarios and is fully launched in two, including +3.57% CTR and +1.71% advertising revenue. BARGE improves Hit@5 by 10.2-16.9% and yields +0.60% CTR and +1.70% reading time after full rollout. HiGR improves offline slate quality by 15.9-21.3% with a 5x inference speedup and achieves up to +1.22% watch time and +1.73% video views. TGR-Reason raises cold-start new-user Hit@1 by 477.8% and delivers +1.75% effective consumption and +13.09% new-user exposure-to-conversion online.
Tgr Team Lei Cheng, Hao-Nan Hu, Beibei Kong et al.· 0 citations
Multimodal information can improve the accuracy of click-through rate (CTR) prediction and effectively alleviate item cold-start and long-tail problems. Recent studies commonly discretize pretrained multimodal embeddings into semantic identifiers (SIDs), allowing the model to learn task-specific semantic representations for recommendation. However, existing methods still provide limited gains due to two major limitations. First, codebook assignment fails to preserve semantic relevance and discards fine-grained continuous signals in the original embedding space. Second, the residual code paths are highly dependent on prefix codes, which limits the effective representational scalability of hierarchical identifiers. To address these issues, we propose PaletteID (PID), a prototype-based semantic identifier. Inspired by palette-based color composition, PID uses a compact set of representative prototype items as semantic anchors to bridge pretrained multimodal content space and recommendation models. Specifically, we first construct a prototype palette with Semantic Quality-Aware Determinantal Point Process (SQ-DPP), which jointly considers local content density and global semantic diversity. Then, for each target item, PID retrieves a sequence of semantically related prototypes and aggregates them into an informative PID representation, enabling rich and complementary semantic modeling. Extensive experiments on two public datasets demonstrate that PID consistently improves CTR prediction and yields larger gains for long-tail items. PID also produces more robust identifier assignments and provides more interpretable token semantics than existing residual SID methods.
Huan-Yu Liu, Baining Chen, Hui Liu et al.· arXiv.org· 0 citations
Large Language Models (LLMs) have demonstrated remarkable general-purpose abilities across a wide range of domains, and these strengths have also been increasingly evidenced in recommender systems. However, existing methods that attempt to integrate collaborative signals into LLMs often fail to preserve their foundational knowledge. This loss is critical in text-rich recommendation, where robust semantic understanding is required to interpret user reviews and item profiles. We propose PALRec, a parameter-preserving augmentation framework that equips an LLM with recommendation capabilities while keeping its original parameters fixed. We first construct evidence-grounded user and item profiles from reviews and use them as concise pseudo-labels for reconstruction. We then introduce lightweight, trainable user and item embedding modules optimized with a multi-task objective that combines next-item prediction and profile reconstruction. These modules are trained jointly to align collaborative signals with the LLM’s semantic space without modifying the backbone. We also employ token-aware loss decomposition and frequency-aware reweighting to stabilize training and mitigate popularity bias. Experiments on public benchmarks show that PALRec consistently outperforms fully fine-tuned counterparts in recommendation accuracy while preserving the LLM’s pre-trained knowledge. This result highlights that maintaining the LLM’s semantic understanding is crucial for effectively exploiting textual information in recommender systems.
Hyunsoo Na, Minseok Gang, Sang-goo Lee et al.· ACM Transactions on Informat...· 0 citations
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