Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs
Authors: Xiaofei Zhu, Jinfei Chen, Feiyang Yuan, Zhou Yang
Organizations: Chongqing University of Technology, College of Computer Science and Engineering, Chongqing, China · Chongqing Normal University, College of Computer and Information Science, Chongqing, China
Recommendation systems aim to learn user interests from historical behaviors and deliver relevant items. Recent methods leverage large language models (LLMs) to construct and integrate semantic representations of users and items for capturing user interests. However, user behavior theories suggest that truly understanding user interests requires not only semantic integration but also semantic reasoning from explicit individual interests to implicit group interests. To this end, we propose an Iterative Semantic Reasoning Framework (ISRF) for generative recommendation. ISRF leverages LLMs to bridge explicit individual interests and implicit group interests in three steps. First, we perform multi-step bidirectional reasoning over item attributes to infer semantic item features and build a semantic interaction graph capturing users' explicit interests. Second, we generate semantic user features based on the semantic item features and construct a similarity-based user graph to infer the implicit interests of similar user groups. Third, we adopt an iterative batch optimization strategy, where individual explicit interests directly guide the refinement of group implicit interests, while group implicit interests indirectly enhance individual modeling. This iterative process ensures consistent and progressive interest reasoning, enabling more accurate and comprehensive user interest learning. Extensive experiments on the Sports, Beauty, and Toys datasets demonstrate that ISRF outperforms state-of-the-art baselines. The code is available at https://github.com/htired/ISRF.
Figures & tables
Figure 1. An illustrative comparison between semantic integration approach and our proposed ISRF (Semantic Reasoning). An illustrative comparison between semantic integration approach and our proposed ISRF (Semantic Reasoning).
Figure 2. The overall architecture of our proposed Iterative Semantic Reasoning Framework (ISRF), which includes: (a) Individual Interest Reasoning; (b) Group Interest Reasoning; (c) Iterative Refinement. The overall architecture of our proposed Iterative Semantic Reasoning Framework (ISRF)
Models
Sports
Beauty
Toys
Metrics
H@5
N@5
H@10
N@10
H@5
N@5
H@10
N@10
H@5
N@5
H@10
N@10
Caser
0.0116
0.0072
0.0194
0.0097
0.0131
0.0087
0.0176
0.0101
0.0166
0.0107
0.0270
0.0141
GRU4Rec
0.0129
0.0086
0.0204
0.0099
0.0200
0.0283
0.0137
0.0200
0.0099
0.0059
0.0176
0.0084
HGN
0.0189
0.0120
0.0313
0.0163
0.0512
0.0266
0.0263
0.0455
0.0201
0.0141
0.0170
0.0300
SASRec
0.0233
0.0154
0.0350
0.0192
0.0500
0.0347
0.0170
0.0650
0.0463
0.0306
0.0675
0.0374
BERT4Rec
0.0115
0.0075
0.0191
0.0099
0.0203
0.0124
0.0347
0.0170
0.0116
0.0071
0.0203
0.0099
Table 1. Performance comparison on the sequential recommendation task, where “*” indicates that the improvement is statistically significant ( p -value <0.05 ) under a 5-trial t -test.
Models
Sports
Beauty
Toys
Metrics
H@5
N@5
H@10
N@10
H@5
N@5
H@10
N@10
H@5
N@5
H@10
N@10
SampleX
0.2362
0.1505
0.3290
0.1800
0.2247
0.1441
0.3090
0.1711
0.1958
0.1244
0.2662
0.1469
LightGCN
0.4150
0.3002
0.5436
0.3418
0.4205
0.3067
0.5383
0.3451
0.3879
0.2874
0.5106
0.3272
NCL
0.4292
0.3131
0.5592
0.3551
0.4378
0.3228
0.5542
0.3607
0.3975
0.2925
0.5120
0.3325
XSimGCL
0.3547
0.2689
0.4486
0.2992
0.3530
0.2734
0.4392
0.3012
0.3351
0.2614
0.4186
0.2885
P5
0.1955
0.1355
0.2802
0.1627
0.1564
0.1096
0.2300
0.1332
0.1322
0.0889
0.2023
0.1114
Table 2. Performance comparison on direct recommendation task.
Ablation
Toys
Beauty
Sports
H@10
N@10
H@10
N@10
H@10
N@10
Sequential Recommendation
ISRF
0.0792
0.0652
0.0800
0.0571
0.0639
0.0488
w/o LD→S
0.0779
0.0636
0.0779
0.0553
0.0601
0.0464
w/o LS
0.0775
0.0636
0.0771
0.0548
0.0614
0.0469
Direct Recommendation
Table 3. Ablation studies on Direct Recommendation and Sequential Recommendation tasks across different components of ISRF, evaluated using Hit Rate@10 (H@10) and NDCG@10 (N@10).
Figure 3. The performance of ISRF and its semantic variants. The performance of ISRF and its semantic variants.
Figure 4. The hyper-parameter study focuses on the L′ . The hyper-parameter study focuses on the L′.
Datsets
Models
Train Time
GPU Memory
Infer DR
Infer SR
Sports
ELMRec
10m10s/epoch
23.58 GB
24m07s
13m30s
ISRF
15m01s/epoch
24.19 GB
20m23s
13m42s
Beauty
ELMRec
6m13s/epoch
21.93 GB
12m44s
8m19s
ISRF
8m04s/epoch
22.12 GB
12m34s
8m22s
Toys
ELMRec
5m07s/epoch
21.15 GB
9m59s
7m29s
ISRF
5m42s/epoch
21.78 GB
9m52s
7m23s
Table 4. Computational Cost Comparison. Infer DR and Infer SR denote the inference time of Direct Recommendation and Sequential Recommendation, respectively.
Figure 5. Case study on identifying users’ implicit interests. Case study on identifying users’ implicit interests.
We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresses the "Shallow Interest" problem - existing generative methods rely on surface-level textual features and fail to capture latent user motivations, limiting personalization depth and recommendation interpretability. Our approach leverages Multi-LLM Interest Mining (MLIM) via structured reasoning prompting, Reward-Labeled Deep Interest (RLDI) for quality control, and Interest-Enhanced Item Discretization (IEID) via RQ-VAE, combined with a two-stage SFT-GRPO training pipeline guided by an Interest-Aware Reward. We validate DeepInterestGR on three Amazon Review benchmarks (Beauty, Sports, Instruments), comparing against 14 state-of-the-art baselines including SASRec, BERT4Rec, TIGER, LC-Rec, and S-DPO. Our method achieves 5.8%-8.3% relative improvements on HR@10 and 7.7%-9.9% on NDCG@10 over the strongest baseline, with cross-domain generalization gains of +24.8%. These results provide evidence that incorporating deep semantic interests can effectively improve SID-based generative recommendation.
Large Language Models (LLMs) have shown strong potential for recommendation by leveraging their semantic understanding and contextual modeling capabilities. Recent studies further introduce reasoning mechanisms to improve user preference modeling. However, explicit natural-language reasoning incurs substantial inference overhead, whereas existing latent reasoning methods mainly focus on generating or verifying intermediate states, leaving their layer-wise preference roles and contributions insufficiently characterized. We propose HiLaR, a Hierarchical Latent Reasoning framework with layer-aware reinforcement optimization for LLM-based recommendation. HiLaR constructs temporal-guided hierarchical user preference representations, aligns them with multiple LLM latent reasoning states, and organizes the reasoning process from broad preferences to fine-grained current intents. To further optimize the reasoning trajectory, HiLaR combines final recommendation feedback with layer-aware process rewards derived from the marginal target-likelihood gain of each state. Experiments on four Amazon benchmark datasets show that HiLaR generally outperforms strong sequential, generative, and LLM-based recommendation baselines. Ablation and sensitivity analyses further verify the contribution of hierarchical representation learning, latent alignment, and process-level optimization. Our code is available in https://github.com/hupeiyu21/HiLaR.
Peiyu Hu, Siying Gu, Weihai Lu +8
1Xi’an Jiaotong-Liverpool University · 2Xiaohongshu · 3Peking University +1
Generative recommendation is an emerging paradigm that has shown promise in industrial recommendation systems, aiming to predict users' next interactions from their historical behaviors. At the core of generative recommendation lies item tokenization, which bridges item semantics and recommendation models. However, existing methods often struggle to effectively organize and inject complex user-behavioral and item-semantic contexts into recommendation models simultaneously. On the one hand, existing graph-based integration methods, such as graph serialization and graph neural networks, either suffer from scalability issues or exploit only local graph information. On the other hand, existing semantic tokenization methods typically rely on heuristics and lack explicit supervision signals, which may lead to inaccurate or suboptimal semantic representations. To address these limitations in user interest context modeling, we propose G2Rec, a scalable framework that unifies holistic graph-based user co-engagement modeling with semantic tokenization for industrial-scale generative recommendation. Overall, G2Rec enables recommendation models to capture holistic and semantically grounded user interest prototypes without requiring ground-truth user interests, thereby providing more comprehensive and accurate modeling of user behavior contexts in industrial sequential recommendation. Online deployment across product surfaces and extensive experiments on public datasets demonstrate the superiority of G2Rec over existing methods.
Ruizhong Qiu, Yinglong Xia, Dongqi Fu +6
University of Illinois Urbana–Champaign · 2Meta MRS