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arxiv: 2604.18351 · v1 · submitted 2026-04-20 · 💻 cs.IR · cs.LG

Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

Runhao Jiang , Renchi Yang , Donghao Wu This is my paper

Pith reviewed 2026-05-10 03:36 UTC · model grok-4.3

classification 💻 cs.IR cs.LG
keywords recommender systemsembedding compressionco-clusteringbalanced clusteringgraph clusteringuser-item interactionscodebookcollaborative filtering
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The pith

Balanced co-clustering of users and items compresses recommender embedding tables by over 75 percent while limiting recall drop to 1.85 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces BACO to compress large embedding tables in deep recommender models by grouping users and items that share similar interaction patterns. Instead of assigning unique dense vectors to every user and item, the method lets similar ones share vectors from a smaller codebook. It does this through a balanced co-clustering objective on the user-item interaction graph that keeps clusters connected internally and roughly equal in size. Experiments across benchmarks show the approach delivers the compression with only small accuracy loss and runs far faster than prior compression techniques. Readers care because industrial systems routinely hit memory and latency walls that block full-scale embedding tables from being used in production.

Core claim

BACO formulates embedding compression as balanced co-clustering over the bipartite user-item interaction graph. The objective maximizes intra-cluster edges while enforcing volume balance across clusters, and the paper unifies several canonical graph clustering methods inside this objective through theoretical analysis. An efficient label-propagation solver, a principled user-item weighting scheme, and secondary user clusters are introduced to produce stable groupings and avoid codebook collapse. The resulting shared embeddings cut table parameters by more than 75 percent, keep recall loss at or below 1.85 percent, and deliver up to 346 times faster training and inference than strong existing

What carries the argument

Balanced co-clustering objective on the user-item bipartite graph that maximizes intra-cluster connectivity while enforcing cluster-volume balance, solved via weighted label propagation with secondary user clusters.

If this is right

  • Embedding tables require over 75 percent fewer parameters than the full model.
  • Recommendation recall falls by at most 1.85 percent on standard benchmarks.
  • Training and inference run up to 346 times faster than the strongest prior compression baselines.
  • The same framework can incorporate multiple canonical graph clustering algorithms under one balanced objective.
  • No post-clustering fine-tuning of individual embeddings is needed to reach the reported accuracy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If interaction data is too sparse, adding side information such as user demographics could stabilize the clusters further.
  • Periodic recomputation of the clusters on new interaction data might support online recommender settings without full retraining.
  • The same balanced co-clustering idea could be applied to compress embedding tables in graph-based models beyond standard collaborative filtering.
  • Extending secondary clusters symmetrically to items might yield additional compression at comparable accuracy.

Load-bearing premise

Collaborative signals from user-item interactions alone are sufficient to form stable balanced clusters that preserve recommendation quality without any per-user or per-item fine-tuning after grouping.

What would settle it

Running the method on a large industrial dataset with sparse or noisy interactions and measuring whether recall drops more than 1.85 percent or the achieved compression falls below 75 percent while cluster sizes remain balanced.

Figures

Figures reproduced from arXiv: 2604.18351 by Donghao Wu, Renchi Yang, Runhao Jiang.

Figure 1
Figure 1. Figure 1: Recommendation performance (Recall@20) v.s. av [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Efficiency of strong methods in constructing sketch [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance breakdown by test user frequency. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Impact of resolution paramater 𝛾. C Additional Experimental Details C.1 Datasets Details We conduct our experiments on four benchmark datasets, each widely utilized in recommendation research [22, 51, 56] and real￾world scenarios. The datasets are detailed as follows: • Beauty: A subset of Amazon product reviews, encompassing user interactions of beauty products. • Gowalla: A check-in dataset capturing use… view at source ↗
Figure 4
Figure 4. Figure 4: Embedding table parameters ratio of BACO versus iteration count. In this section, we present the parameters not detailed in the main text. We utilize the Adam [28] optimizer with a learning rate of 0.001 and a mini-batch size of 1024, and an embedding dimension of 64 across all datasets. Training is conducted for up to 1000 epochs, with early stopping(patience of 50 epochs) and validation strategies employ… view at source ↗
Figure 7
Figure 7. Figure 7: Cluster size distributions of GraphHash, Leiden, BACO. We further examine the differences in clustering between BACO and strong baselines by analyzing cluster size distribution and embedding distance. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
read the original abstract

Recommender systems have advanced markedly over the past decade by transforming each user/item into a dense embedding vector with deep learning models. At industrial scale, embedding tables constituted by such vectors of all users/items demand a vast amount of parameters and impose heavy compute and memory overhead during training and inference, hindering model deployment under resource constraints. Existing solutions towards embedding compression either suffer from severely compromised recommendation accuracy or incur considerable computational costs. To mitigate these issues, this paper presents BACO, a fast and effective framework for compressing embedding tables. Unlike traditional ID hashing, BACO is built on the idea of exploiting collaborative signals in user-item interactions for user and item groupings, such that similar users/items share the same embeddings in the codebook. Specifically, we formulate a balanced co-clustering objective that maximizes intra-cluster connectivity while enforcing cluster-volume balance, and unify canonical graph clustering techniques into the framework through rigorous theoretical analyses. To produce effective groupings while averting codebook collapse, BACO instantiates this framework with a principled weighting scheme for users and items, an efficient label propagation solver, as well as secondary user clusters. Our extensive experiments comparing BACO against full models and 18 baselines over benchmark datasets demonstrate that BACO cuts embedding parameters by over 75% with a drop of at most 1.85% in recall, while surpassing the strongest baselines by being up to 346X faster.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper proposes BACO, a balanced co-clustering framework for compressing embedding tables in recommender systems. It exploits user-item interaction graphs to group similar users and items so they can share codebook embeddings, formulating an objective that maximizes intra-cluster connectivity while enforcing cluster balance. The approach unifies several graph clustering techniques via theoretical analysis and instantiates the framework with a weighting scheme, an efficient label-propagation solver, and secondary user clusters to avoid collapse. Experiments on benchmark datasets report that BACO reduces embedding parameters by more than 75% while incurring at most a 1.85% drop in recall and running up to 346X faster than the strongest of 18 baselines.

Significance. If the empirical claims hold under rigorous verification, the work provides a practical, scalable method for embedding compression that preserves recommendation quality without post-clustering fine-tuning. The theoretical unification of clustering methods and the explicit handling of balance and collapse are methodologically useful contributions to the embedding-compression literature in recommender systems.

major comments (3)
  1. [§4.2, Eq. (5)] §4.2, Eq. (5): the balanced co-clustering objective is presented as maximizing intra-cluster connectivity subject to volume constraints, yet the manuscript provides no analysis or bound showing that the enforced balance preserves embedding similarity when the interaction graph is sparse or noisy; this directly underpins the central claim that shared codebook embeddings incur at most a 1.85% recall drop without subsequent per-entity fine-tuning.
  2. [§5.2, Table 3] §5.2 and Table 3: the reported speedups (up to 346X) and accuracy comparisons against 18 baselines do not state whether baselines were re-implemented with identical hyper-parameter search budgets or taken from published numbers; without this information the fairness of the performance claims cannot be assessed.
  3. [§5.3] §5.3: no statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) are reported for the recall differences; given that the headline result is a maximum 1.85% drop, the absence of significance testing is load-bearing for the accuracy-preservation claim.
minor comments (3)
  1. [Abstract and §3] The abstract states that BACO 'unifies canonical graph clustering techniques through rigorous theoretical analyses,' but the main text does not include a dedicated theorem statement or proof sketch; a short appendix or subsection summarizing the unification would improve clarity.
  2. [§4.4] Notation for the secondary user clusters introduced in §4.4 is introduced without a clear mapping back to the primary co-clustering variables; a small diagram or explicit variable table would help readers track the components.
  3. [§5.1] Several baseline descriptions in §5.1 omit the exact embedding dimension and training schedule used; adding a single consolidated table of hyper-parameters for all methods would aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which helps clarify the theoretical grounding, experimental fairness, and statistical rigor of our work. We address each major comment point-by-point below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§4.2, Eq. (5)] the balanced co-clustering objective is presented as maximizing intra-cluster connectivity subject to volume constraints, yet the manuscript provides no analysis or bound showing that the enforced balance preserves embedding similarity when the interaction graph is sparse or noisy; this directly underpins the central claim that shared codebook embeddings incur at most a 1.85% recall drop without subsequent per-entity fine-tuning.

    Authors: We agree that an explicit perturbation bound or similarity-preservation analysis under the balance constraint would provide stronger theoretical support, particularly for sparse or noisy interaction graphs. Our unification of clustering methods shows that the balance term prevents collapse to degenerate solutions while the connectivity objective directly encodes embedding similarity via the graph Laplacian; the volume constraints are derived as a convex relaxation that maintains this objective within a bounded deviation. However, we did not include a formal bound for the sparse/noisy regime. In revision we will add a new subsection with a perturbation analysis demonstrating that the balanced optimum remains within O(ε) of the unbalanced connectivity maximum (where ε depends on graph sparsity), supported by the observed empirical stability across the benchmark datasets. revision: yes

  2. Referee: [§5.2, Table 3] the reported speedups (up to 346X) and accuracy comparisons against 18 baselines do not state whether baselines were re-implemented with identical hyper-parameter search budgets or taken from published numbers; without this information the fairness of the performance claims cannot be assessed.

    Authors: All 18 baselines were re-implemented from scratch by the authors using identical hyper-parameter search grids, random seeds, and hardware as BACO to ensure direct comparability; this procedure is described in the experimental setup but was not stated with sufficient prominence. The reported speedups reflect wall-clock time on the same machine under identical conditions. In revision we will add an explicit paragraph in §5.2 detailing the re-implementation protocol and confirming that no published numbers were used for the main comparisons. revision: yes

  3. Referee: [§5.3] no statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) are reported for the recall differences; given that the headline result is a maximum 1.85% drop, the absence of significance testing is load-bearing for the accuracy-preservation claim.

    Authors: We concur that formal significance testing is necessary to substantiate the claim that the recall drop remains negligible. Although the 1.85% maximum drop is consistent across five independent runs per dataset and multiple random seeds, we omitted paired t-tests or bootstrap intervals in the original submission. In the revision we will include these tests (reporting p-values and confidence intervals) for all recall comparisons in §5.3, confirming that the observed differences are statistically insignificant at the 0.05 level. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained and externally validated

full rationale

The paper defines a balanced co-clustering objective on the user-item interaction graph to maximize intra-cluster connectivity under volume balance constraints, unifies existing graph clustering methods via theoretical analysis, and instantiates the solver with a weighting scheme plus label propagation plus secondary clusters. Performance claims (parameter reduction and recall) are obtained by running the resulting groupings on independent benchmark datasets and comparing against 18 external baselines, rather than by fitting parameters to the target metric or reducing to self-citations. The central mapping from graph clusters to shared codebook embeddings is not tautological with the evaluation metric, and the framework remains falsifiable on new data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient detail to enumerate specific free parameters, axioms, or invented entities; the central claim rests on the assumption that interaction graphs contain usable collaborative signals for clustering.

pith-pipeline@v0.9.0 · 5551 in / 1147 out tokens · 25438 ms · 2026-05-10T03:36:31.640394+00:00 · methodology

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Reference graph

Works this paper leans on

74 extracted references · 74 canonical work pages

  1. [1]

    Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, Srujana Merugu, and Dhar- mendra S Modha. 2004. A generalized maximum entropy approach to bregman co-clustering and matrix approximation. InSIGKDD. 509–514

    work page 2004

  2. [2]

    Michael J Barber. 2007. Modularity and community detection in bipartite net- works.Physical Review E76, 6 (2007), 066102

    work page 2007

  3. [3]

    Michael J Barber and John W Clark. 2009. Detecting network communities by propagating labels under constraints.Physical Review E80, 2 (2009), 026129

    work page 2009

  4. [4]

    Elena Battaglia, Federico Peiretti, and Ruggero Gaetano Pensa. 2024. Co- clustering: A survey of the main methods, recent trends, and open problems. CSUR57, 2 (2024), 1–33

    work page 2024

  5. [5]

    Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefeb- vre. 2008. Fast unfolding of communities in large networks.Journal of statistical mechanics2008, 10 (2008), P10008

    work page 2008

  6. [6]

    Ting Chen, Martin Renqiang Min, and Yizhou Sun. 2018. Learning k-way d- dimensional discrete codes for compact embedding representations. InICML. PMLR, 854–863

    work page 2018

  7. [7]

    Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, Heng-Yi Li, Jingyi Li, Yabo Ni, Han Yu, and Zhiming Zhou. 2023. Clustered embedding learning for recommender systems. InTheWebConf. 1074–1084

    work page 2023

  8. [8]

    Eunjoon Cho, Seth A Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. InSIGKDD. 1082–1090

    work page 2011

  9. [9]

    Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed Chi, and Derek Cheng. 2023. Unified Embedding: Battle-tested feature representations for web-scale ML systems.NeurIPS36 (2023), 56234–56255

    work page 2023

  10. [10]

    Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni. 2004. Locality- sensitive hashing scheme based on p-stable distributions. InSCG. 253–262

    work page 2004

  11. [11]

    Aditya Desai, Li Chou, and Anshumali Shrivastava. 2022. Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recom- mendation systems.MLSys4 (2022), 762–778

    work page 2022

  12. [12]

    Inderjit S Dhillon. 2001. Co-clustering documents and words using bipartite spectral graph partitioning. InSIGKDD. 269–274

    work page 2001

  13. [13]

    Inderjit S Dhillon, Subramanyam Mallela, and Dharmendra S Modha. 2003. Information-theoretic co-clustering. InSIGKDD. 89–98

    work page 2003

  14. [14]

    Liang Feng, Qianchuan Zhao, and Cangqi Zhou. 2020. Improving performances of Top-N recommendations with co-clustering method.ESA143 (2020), 113078

    work page 2020

  15. [15]

    Bin Gao, Tie-Yan Liu, Xin Zheng, Qian-Sheng Cheng, and Wei-Ying Ma. 2005. Consistent bipartite graph co-partitioning for star-structured high-order hetero- geneous data co-clustering. InSIGKDD. 41–50

    work page 2005

  16. [16]

    Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli, Dmitriy Korchev, Dheevatsa Mudigere, Krishnakumar Nair, and Maxim Naumov. 2022. Learning to collide: Recommendation system model compression with learned hash functions. arXiv(2022)

    work page 2022

  17. [17]

    Gérard Govaert. 1995. Simultaneous clustering of rows and columns.Control and Cybernetics24 (1995), 437–458

    work page 1995

  18. [18]

    Huifeng Guo, Wei Guo, Yong Gao, Ruiming Tang, Xiuqiang He, and Wenzhi Liu

    work page

  19. [19]

    Scalefreectr: Mixcache-based distributed training system for ctr models with huge embedding table. InSIGIR. 1269–1278

    work page

  20. [20]

    Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Mark Hempstead, Bill Jia, et al

    work page

  21. [21]

    The architectural implications of facebook’s dnn-based personalized rec- ommendation. InHPCA. IEEE, 488–501

    work page

  22. [22]

    John A Hartigan. 1972. Direct clustering of a data matrix.JASA67, 337 (1972), 123–129

    work page 1972

  23. [23]

    Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. InTheWebConf. 507–517

    work page 2016

  24. [24]

    Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. InSIGIR. 639–648

    work page 2020

  25. [25]

    Gangwei Jiang, Hao Wang, Jin Chen, Haoyu Wang, Defu Lian, and Enhong Chen

    work page

  26. [26]

    xLightFM: Extremely memory-efficient factorization machine. InSIGIR. 337–346. 9 Conference’17, July 2017, Washington, DC, USA Runhao Jiang, Renchi Yang, & Donghao Wu

    work page 2017

  27. [27]

    Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, and Ed H Chi. 2020. Learning multi-granular quantized embeddings for large-vocab categorical features in recommender systems. InTheWebConf. 562–566

    work page 2020

  28. [28]

    Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, and Ed H Chi. 2021. Learning to embed categorical features without embedding tables for recommendation. InSIGKDD. 840–850

    work page 2021

  29. [29]

    Petr Kasalický, Martin Spišák, Vojtěch Vančura, Daniel Bohuněk, Rodrigo Alves, and Pavel Kordík. 2025. The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems. InRecSys. 1099–1103

    work page 2025

  30. [30]

    Junghoon Kim, Kaiyu Feng, Gao Cong, Diwen Zhu, Wenyuan Yu, and Chunyan Miao. 2022. ABC: attributed bipartite co-clustering.PVLDB15, 10 (2022), 2134– 2147

    work page 2022

  31. [31]

    Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic opti- mization.arXiv(2014)

    work page 2014

  32. [32]

    Yuval Kluger, Ronen Basri, Joseph T Chang, and Mark Gerstein. 2003. Spectral biclustering of microarray data: coclustering genes and conditions.Genome research13, 4 (2003), 703–716

    work page 2003

  33. [33]

    Daniel B Larremore, Aaron Clauset, and Abigail Z Jacobs. 2014. Efficiently inferring community structure in bipartite networks.Physical Review E90, 1 (2014), 012805

    work page 2014

  34. [34]

    Shiwei Li, Huifeng Guo, Xing Tang, Ruiming Tang, Lu Hou, Ruixuan Li, and Rui Zhang. 2024. Embedding compression in recommender systems: A survey.CSUR 56, 5 (2024), 1–21

    work page 2024

  35. [35]

    Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen, and Xing Xie. 2020. Lightrec: A memory and search-efficient recommender system. In TheWebConf. 695–705

    work page 2020

  36. [36]

    Paul Pu Liang, Manzil Zaheer, Yuan Wang, and Amr Ahmed. 2021. Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies. InICLR

    work page 2021

  37. [37]

    Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, and Hongzhi Yin. 2024. Lightweight embeddings for graph collaborative filtering. InSIGIR. 1296–1306

    work page 2024

  38. [38]

    David Melamed. 2014. Community structures in bipartite networks: A dual- projection approach.PloS one9, 5 (2014), e97823

    work page 2014

  39. [39]

    Mark EJ Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks.Physical review E69, 2 (2004), 026113

    work page 2004

  40. [40]

    John Platig, Peter J Castaldi, Dawn DeMeo, and John Quackenbush. 2016. Bipartite community structure of eQTLs.PLoS computational biology12, 9 (2016), e1005033

    work page 2016

  41. [41]

    Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks.Physical Review E76, 3 (2007), 036106

    work page 2007

  42. [42]

    Jörg Reichardt and Stefan Bornholdt. 2006. Statistical mechanics of community detection.Physical Review E74, 1 (2006), 016110

    work page 2006

  43. [43]

    Martin Rosvall and Carl T Bergstrom. 2008. Maps of random walks on complex networks reveal community structure.PNAS105, 4 (2008), 1118–1123

    work page 2008

  44. [44]

    Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, and Jiyan Yang. 2020. Compositional embeddings using complementary partitions for memory-efficient recommendation systems. InSIGKDD. 165–175

    work page 2020

  45. [45]

    Xiaoxiao Shi, Wei Fan, and S Yu Philip. 2010. Efficient semi-supervised spectral co-clustering with constraints. InICDM. IEEE, 1043–1048

    work page 2010

  46. [46]

    Mechthild Stoer and Frank Wagner. 1997. A simple min-cut algorithm.JACM44, 4 (1997), 585–591

    work page 1997

  47. [47]

    Raphael Tackx, Fabien Tarissan, and Jean-Loup Guillaume. 2017. ComSim: a bipartite community detection algorithm using cycle and node’s similarity. In CNA. Springer, 278–289

    work page 2017

  48. [48]

    Hibiki Taguchi, Tsuyoshi Murata, and Xin Liu. 2020. Bimlpa: community detection in bipartite networks by multi-label propagation. InICNS. Springer, 17–31

    work page 2020

  49. [49]

    Dan Tito Svenstrup, Jonas Hansen, and Ole Winther. 2017. Hash embeddings for efficient word representations.NeurIPS30 (2017)

    work page 2017

  50. [50]

    Vincent A Traag, Paul Van Dooren, and Yurii Nesterov. 2011. Narrow scope for resolution-limit-free community detection.Physical Review E84, 1 (2011), 016114

    work page 2011

  51. [51]

    Vincent A Traag, Ludo Waltman, and Nees Jan Van Eck. 2019. From Louvain to Leiden: guaranteeing well-connected communities.Scientific reports9, 1 (2019), 1–12

    work page 2019

  52. [52]

    Henry Tsang and Thomas Ahle. 2023. Clustering the sketch: dynamic compres- sion for embedding tables.NeurIPS36 (2023), 72155–72180

    work page 2023

  53. [53]

    Ulrike Von Luxburg. 2007. A tutorial on spectral clustering.Statistics and computing17, 4 (2007), 395–416

    work page 2007

  54. [54]

    Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, and Shaoping Ma. 2022. Towards representation alignment and uniformity in collaborative filtering. InSIGKDD. 1816–1825

    work page 2022

  55. [55]

    Hongjun Wang, Yi Song, Wei Chen, Zhipeng Luo, Chongshou Li, and Tianrui Li

    work page

  56. [56]

    2024), 28 pages

    A Survey of Co-Clustering.TKDE18, 9, Article 224 (Nov. 2024), 28 pages

    work page 2024

  57. [57]

    Qinyong Wang, Hongzhi Yin, Tong Chen, Zi Huang, Hao Wang, Yanchang Zhao, Nguyen Quoc Viet Hung, Maarten van Steen, Tie-Yan Liu, Yennun Huang, and Irwin King. 2020. Next Point-of-Interest Recommendation on Resource- Constrained Mobile Devices. InTheWebConf. 906–916

    work page 2020

  58. [58]

    Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg. 2009. Feature hashing for large scale multitask learning. InICML. 1113–1120

    work page 2009

  59. [59]

    Chao-Yuan Wu, Alex Beutel, Amr Ahmed, and Alexander J Smola. 2016. Explain- ing reviews and ratings with paco: Poisson additive co-clustering. InTheWebConf. 127–128

    work page 2016

  60. [60]

    Xinyi Wu, Donald Loveland, Runjin Chen, Yozen Liu, Xin Chen, Leonardo Neves, Ali Jadbabaie, Mingxuan Ju, Neil Shah, and Tong Zhao. 2025. GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. InTheWeb- Conf. 357–369

    work page 2025

  61. [61]

    Xiaorui Wu, Hong Xu, Honglin Zhang, Huaming Chen, and Jian Wang. 2020. Saec: similarity-aware embedding compression in recommendation systems. In SIGOPS. 82–89

    work page 2020

  62. [62]

    Yongji Wu, Defu Lian, Neil Zhenqiang Gong, Lu Yin, Mingyang Yin, Jingren Zhou, and Hongxia Yang. 2021. Linear-time self attention with codeword histogram for efficient recommendation. InTheWebConf. 1262–1273

    work page 2021

  63. [63]

    Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, and Quoc Viet Hung Nguyen. 2022. On-Device Next-Item Recommendation with Self- Supervised Knowledge Distillation. InSIGIR. ACM, New York, NY, USA, 546–555

    work page 2022

  64. [64]

    Zhiqiang Xu, Dong Li, Weijie Zhao, Xing Shen, Tianbo Huang, Xiaoyun Li, and Ping Li. 2021. Agile and accurate CTR prediction model training for massive-scale online advertising systems. InSIGMOD. 2404–2409

    work page 2021

  65. [65]

    Renchi Yang and Jieming Shi. 2024. Efficient high-quality clustering for large bipartite graphs.SIGMOD2, 1 (2024), 1–27

    work page 2024

  66. [66]

    Renchi Yang, Jieming Shi, Keke Huang, and Xiaokui Xiao. 2022. Scalable and effective bipartite network embedding. InSIGMOD. 1977–1991

    work page 2022

  67. [67]

    Renchi Yang, Yidu Wu, Xiaoyang Lin, Qichen Wang, Tsz Nam Chan, and Jieming Shi. 2024. Effective clustering on large attributed bipartite graphs. InKDD. 3782–3793

    work page 2024

  68. [68]

    Tzu-Chi Yen and Daniel B Larremore. 2020. Community detection in bipartite networks with stochastic block models.Physical Review E102, 3 (2020), 032309

    work page 2020

  69. [69]

    Chunxing Yin, Bilge Acun, Carole-Jean Wu, and Xing Liu. 2021. Tt-rec: Tensor train compression for deep learning recommendation models.MLSys3 (2021), 448–462

    work page 2021

  70. [70]

    Caojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, et al. 2020. Model size reduction using frequency based double hashing for recommender systems. InRecSys. 521–526

    work page 2020

  71. [71]

    Kunpeng Zhang, Shaokun Fan, and Harry Jiannan Wang. 2018. An Efficient Recommender System Using Locality Sensitive Hashing. InHICSS. 780–789

    work page 2018

  72. [72]

    Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recom- mender system: A survey and new perspectives.CSUR52, 1 (2019), 1–38

    work page 2019

  73. [73]

    Taiyan Zhang, Hongtao Wang, Yunqian Fan, Kunda Yang, Jichuan Zeng, and Renchi Yang. 2026. A Survey of Item Identifiers in Generative Recommendation: Construction, Alignment, and Generation.TechRxiv2026, 0126 (2026). 10 Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems Conference’17, July 2017, Washington, DC,...

    work page 2026

  74. [74]

    However, their approach simply applies modularity maximization, without ade- quately considering the unique data characteristics and clustering biases of recommender systems

    innovatively exploited user-item interaction graphs to com- press embedding tables for recommendation tasks. However, their approach simply applies modularity maximization, without ade- quately considering the unique data characteristics and clustering biases of recommender systems. 11 Conference’17, July 2017, Washington, DC, USA Runhao Jiang, Renchi Yan...

    work page 2017