Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non-uniform computational need: items within the same sequence vary drastically in how much computation they require. Most converge after shallow processing while a critical minority only stabilizes at full depth. (2) Suboptimality of fixed allocation: given oracle access to the target item, a non-uniform depth assignment consistently outranks the fixed one, indicating that existing uniform computation both wastes capacity on already-converged items and sacrifices overall recommendation quality. These observations motivate a shift from fixed-depth computation to item-level adaptive reasoning. To this end, we propose LoopRec. LoopRec recursively applies a single weight-shared block and introduces a lightweight gate at each recursion boundary that makes a binary exit-or-continue decision for every item independently. Items whose representations have sufficiently matured exit early, while those requiring deeper reasoning continue to subsequent recursions. This design converts the fixed global depth into a learned, per-item property without auxiliary depth labels, and is readily adaptable to diverse backbone architectures. Extensive experiments on public benchmarks with four backbone families demonstrate that LoopRec consistently outperforms matched fixed-depth counterparts. Further analysis reveals that LoopRec's learned allocation exhibits interpretable patterns aligned with our post-hoc oracle analysis: lower-frequency items and more recent interactions naturally receive deeper computation. Our code is available at https://github.com/Elpmis117/LoopRec.
Figures & tables
Figure 1. Item saturation depth ℓi⋆ on Office. (a) Item-level distribution across the test set. (b) Per-sequence depth composition. Each violin shows the distribution of within-sequence shares, with dots and bars marking medians and IQRs.
Sequence
Seq. 1
Seq. 2
Seq. 3
Seq. 4
Seq. 5
Rank
24→13
7→4
7→5
12→10
19→18
Gain
+11
+3
+2
+2
+1
Rel. tail comp.
40.0%
66.7%
53.3%
60.0%
60.0%
Table 1. Rank improvements and relative tail computation for the sequences in Figure 2 (b), normalized by the fixed-depth allocation.
Figure 2. Restricted tail-5 oracle on Office. (a) Relative gains of the flexible oracle over the fixed-depth baseline. (b) Oracle-selected tail depths for sample sequences. Numbers denote depths, and columns 0–4 are the five most recent positions.
Figure 3. Overview of LoopRec. At each recursion boundary, depth-routing gates decide which positions stop and which continue through the shared block.
Dataset
Users
Items
Interactions
Avg. Len.
Sparsity
Software
13,444
9,706
227,637
16.93
99.83%
Office
11,468
28,279
190,465
16.61
99.94%
Patio
14,454
37,179
229,086
15.85
99.96%
Table 2. Statistics of experimental datasets.
Backbone
Model
Software
Office
Patio
M@10
M@20
N@10
N@20
M@10
M@20
N@10
N@20
M@10
M@20
N@10
N@20
SASRec
Vanilla
0.0589
0.0629
0.0796
0.0944
0.0508
0.0513
0.0562
0.0582
0.0394
0.0399
0.0429
0.0448
+LoopRec
0.0613
0.0659
0.0830
0.0998
0.0523
0.0530
0.0571
0.0595
0.0411
0.0416
0.0441
0.0458
Improv.
+4.14%*
+4.77%*
+4.27%*
+5.68%*
+3.03%*
+3.24%*
+1.49%*
+2.20%*
+4.42%*
+4.26%*
+2.75%*
+2.19%*
BSARec
Vanilla
0.0607
0.0657
0.0803
0.0987
0.0508
0.0516
0.0565
0.0596
0.0391
0.0398
0.0426
0.0453
+LoopRec
0.0619
0.0665
0.0828
0.0998
0.0519
0.0527
0.0571
0.0602
0.0415
0.0422
0.0445
0.0469
Table 3. Main results across three Amazon benchmarks, comparing each fixed-depth backbone with its LoopRec counterpart. M@K and N@K denote MRR@K and NDCG@K, and values are test means over five runs. The “*” denotes the statistical significance ( p<0.05 ) of the results of LoopRec compared to the corresponding fixed-depth backbone under a paired t-test. Within each backbone block, the better value is shown in bold .
Figure 4. Average selected recursion round across relative-position deciles (D1: earliest, D10: most recent), on Office and Patio under two LxRy configurations.
Train freq.
Tokens
Mean depth
Depth 1
Depth 4
1
505
2.160
50.3%
29.1%
2–4
1,202
2.084
53.7%
27.0%
5–9
1,603
2.135
53.3%
29.3%
10–19
2,247
2.008
60.8%
28.1%
20–49
4,123
1.818
69.2%
24.1%
50–99
3,668
1.577
79.1%
17.7%
Table 4. Selected depth by item training-time frequency on Office. Depth columns report the share of tokens assigned to each depth.
Figure 5. Saturation depth on Office under a three-layer SASRec. (a) Mean depth across relative-position quartiles. (b) Depth shares across training-frequency buckets.
Model
Software
Office
Patio
M@20
N@20
M@20
N@20
M@20
N@20
LoopRec
0.0659
0.0998
0.0530
0.0595
0.0416
0.0458
Seq-level Loop
0.0634
0.0950
0.0525
0.0594
0.0390
0.0447
Vanilla
0.0629
0.0944
0.0513
0.0582
0.0399
0.0448
Table 5. Comparison of sequence-level looping and item-level depth allocation. Vanilla denotes the original SASRec. Results are five-seed test means. M and N denote MRR and NDCG, respectively.
Ablation
Software
Office
Patio
M@20
N@20
M@20
N@20
M@20
N@20
LoopRec
0.0659
0.0998
0.0530
0.0595
0.0416
0.0458
w/o warm-up
0.0615
0.0942
0.0520
0.0583
0.0401
0.0450
w/o Lbal
0.0655
0.0995
0.0507
0.0576
0.0385
0.0431
w/o Lz
0.0639
0.0973
0.0507
0.0574
0.0387
0.0432
w/o Lbal & Lz
0.0632
0.0963
0.0505
0.0574
0.0386
0.0431
Table 6. Ablation of routing warm-up and router-side regularizers. Results are five-seed test means. M and N denote MRR and NDCG, respectively.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 6. Selected-depth distributions on Office for four LxRy configurations. The y-axis is the per-sequence share of positions at each effective depth.
Pos.
Item
Category
Freq.
Depth
1
ValueMailers poly mailers
Envelope mailer
56
1
2
Uboxes poly mailers
Envelope mailer
261
1
3
ValueMailers poly mailers
Envelope mailer
517
1
4
EasyPAG desk organizer
Desk organizer
5
1
5
Quartet whiteboard
Dry erase board
259
1
6
Aviditi box mailers
Box mailer
1
2
Appendix
Table 7. Item-level routing of one Office sequence. Freq. is training-time interaction frequency. Depth is the selected depth.
Figure 7. Category-level routed depth versus item popularity on Office. Each point is a category leaf (size ∝ token count). Blue and orange mark the five highest- and lowest-depth categories, and the solid line shows the overall trend.
Method
MRR@10
MRR@20
NDCG@10
NDCG@20
Vanilla
0.0508
0.0513
0.0562
0.0582
Position
0.0508
0.0515
0.0558
0.0585
Frequency
0.0510
0.0516
0.0561
0.0584
LoopRec
0.0523
0.0530
0.0571
0.0595
Appendix
Table 8. Comparison with heuristic routing on Office using SASRec. Values are test means over five seeds. Bold values denote the highest mean for each metric.
Setting
Ref.
Compute
Ref. ms
LoopRec ms
Δ ms
Latency
L2R4
SASRec-8L
0.407 ×
91.230
79.273
−11.957
0.869 ×
L2R3
SASRec-6L
0.680 ×
69.684
63.196
−6.488
0.907 ×
L2R2
SASRec-4L
0.755 ×
47.624
47.379
−0.245
0.995 ×
Appendix
Table 9. Compute budget and encoder latency on Office (single A6000 GPU). Compute and Latency are normalized by the matched fixed-depth reference. Ref. ms and LoopRec ms are median encoder times.
Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns. In this work, we revisit sequential recommendation from a recursive inference perspective: can user preferences be modeled as a persistent latent state that is recursively refined? We propose RecRec (Recursive Recommendation), a lightweight model that maintains a compact latent state and updates it through a shared recursive module conditioned on interaction evidence. Unlike prior recursive models, RecRec introduces an evidence-anchored correction mechanism that stabilizes refinement by grounding each update in the original interaction context, preventing semantic drift during deep recursive reasoning. Experiments on three benchmark datasets under standard evaluation protocols show that RecRec matches or outperforms state-of-the-art sequential, graph-based, and reasoning-enhanced recommenders while using only 3.9M to 14M parameters. Ablation studies demonstrate that both recursive refinement and the evidence-anchored correction gate contribute significantly to performance, highlighting the effectiveness of recursive latent inference as a scalable alternative to deeper or language-based architectures. Code is available at https://anonymous.4open.science/r/RecRec-6B67/README.md.
Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task. Previous work has improved personalization by incorporating collaborative and sequential signals through input conditioning or LLM fine-tuning. However, existing approaches often rely on one or more of the following: LLM fine-tuning, additional architectural modules, representation distillation, or item-level conditioning over long interaction histories, increasing training complexity and deployment cost. We propose REPREC, a lightweight framework that reformulates LLM-based sequential recommendation through lightweight user representation alignment. REPREC maps a fixed-size user embedding from a frozen sequential encoder into a small set of learned soft tokens through a lightweight MLP injector that conditions a frozen LLM, leaving both pretrained backbones unchanged while training only the injector. We conducted exhaustive experiments on multiple benchmark datasets and demonstrate that REPREC consistently outperforms LoRA while remaining compatible with different pretrained sequential encoders and LLM backbones, enabling a modular and production-friendly recommendation pipeline without modifying either pretrained component. The gains are particularly pronounced for casual and core users across all datasets, highlighting REPREC's effectiveness in low-data regimes. Finally, when trained on short prompt histories and evaluated with longer contexts, REPREC maintains 85-100% of LoRA's performance while reducing per-epoch training time by an average of 1.51X, demonstrating an effective balance between recommendation quality and computational efficiency for production deployment. The code is available at https://github.com/phdbotcode/REPREC
Harshini Kavuru, Dwipam Katariya, Giri Iyengar +3
The Ohio State University, USA · Capital One, AI Foundations, USA
Attribute-aware sequential recommendation entails predicting the next item a user will interact with based on a chronologically ordered history of past interactions, enriched with item attributes. Existing methods typically leverage self-attention mechanisms to aggregate the entire sequence into a unified representation used for next-item prediction. While effective, these models often suffer from high computational complexity and memory consumption, limiting their ability to process long user histories. This constraint restricts the model's capacity to fully capture long-term user preferences. In some scenarios, modeling item interactions purely through attention may also not be the most effective approach to extract sequential patterns. In this work, we propose ConvRec, an alternative method with linear computational and memory complexity that employs convolutional layers in a hierarchical, down-scaled fashion to generate compact, yet expressive sequence representations. To further enhance the model's ability to capture diverse sequential patterns, each layer aggregates the neighboring items gradually to reach a comprehensive sequence representation. Extensive experiments on four real-world datasets demonstrate that our approach outperforms state-of-the-art sequential recommendation models, highlighting the potential of convolution-based architectures for efficient and effective sequence modeling in recommendation systems. Our implementation code and datasets are available here https://github.com/ismll-research/ConvRec.
Shereen Elsayed, Ngoc Son Le, Ahmed Rashed +1
Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany · VWFS Data Analytics Research Center (VWFS DARC), University of Hildesheim, Germany · Volkswagen Financial Services AG, Germany