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REVIEW 4 major objections 4 minor 2 cited by

One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read One hierarchical model with per-domain expert masks and counterfactual augmentation claims the best multi-domain recommendation results across 25 Amazon domains and 30 AliCCP domains.

desk verdict A credible method for many-domain recommendation, but the SOTA claim rests on a comparison that pre-clusters every baseline into three pseudo-domains, which is a real flaw. read the letter →

arxiv 2412.11905 v2 pith:GI4325QY submitted 2024-12-16 cs.IR

Huishi Luo , Yiwen Chen , Yiqing Wu , Fuzhen Zhuang , Deqing Wang This is my paper · ORCID
classification cs.IR
keywords multi-domainrecommendationexpertmaskpruningcounterfactualdataaugmentationhierarchicalexpertssparsitylotterytickethypothesisclick-throughratepredictionminordomains
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to show that multi-domain recommendation can work at the scale of dozens of domains, not just the two to five domains most prior methods handle, using a single model. Its proposed framework, AREAD, replaces per-domain output towers with a shared hierarchy of expert networks, learns a sparse selection mask over those experts for each domain, and augments data-sparse minor domains by transferring interactions with unpopular items from major domains under a counterfactual assumption. The claim is that this combination lifts overall accuracy while specifically improving the weakest, least-populated domains. A sympathetic reader would care because real platforms segment items into many categories, and current methods either explode in parameters or let large domains dominate training. If AREAD is right, one unified model can serve dozens of domains at lower maintenance cost without sacrificing the long tail.

What carries the argument

The central object is a three-part architecture. Hierarchical Expert Integration (HEI) stacks a small number of coarse experts at the bottom layer and progressively more, narrower experts at higher layers, capturing domain knowledge at different granularities without a tower per domain. Hierarchical Expert Mask Pruning (HEMP) generates candidate binary masks over the gating edges between expert layers and iteratively prunes the lowest-magnitude gates, in the spirit of lottery-ticket pruning, then keeps the candidate mask with the best domain evaluation; this is what decides, for each domain, which experts to use. The Popularity-based Counterfactual Augmenter defines an unpopular-item threshold and copies positive interactions with unpopular items from major domains into minor domains, justified by the causal claim that such interactions reflect genuine interest rather than conformity. Together these components turn the problem of which domains should share knowledge into a per-domain mask-search problem.

What would settle it

Compare the conditional probability that a user who bought an unpopular item in a major domain later interacts with a corresponding unpopular item in a minor domain against the base interaction rate in that minor domain. If the conditional probability is no higher than the base rate for a held-out cohort, the counterfactual transfer assumption fails and the augmentation would be adding noise rather than signal.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that AREAD achieves the best multi-domain recommendation accuracy on two public benchmarks with more than twenty domains each: 25 Amazon domains and 30 AliCCP domains. Relative to the best baselines, it reports total AUC gains of 6.7 per mille on Amazon and 3 per mille on AliCCP, both with p-value below 0.01, and larger relative gains on metrics that isolate the smallest domains. The mechanism is adaptive per-domain expert selection: each domain gets a binary mask that prunes the gating connections in a hierarchical expert network, so domains are learned neither fully shared nor fully separated, and the mask search is decided by which subnetwork performs best. Counterfactual augmentation then supplies extra training signal to minor domains before their masks are finalized. The paper presents this as a scalable alternative to per-domain towers, pre-clustering, and hand-designed knowledge-sharing rules.

Load-bearing premise

The load-bearing premise is the counterfactual corollary: if a user has positively interacted with an unpopular item in a major domain, the same interaction is likely to occur in a minor domain; if that cross-domain transfer of genuine interest is wrong, the augmentation teaches minor domains from interactions users would not actually have there.

Editorial extensions

If this is right

  • Multi-domain recommenders can scale to dozens of domains without per-domain tower networks; parameter count grows with the chosen expert hierarchy, not with domain count.
  • Data-sparse domains receive measurable accuracy gains from the same model, because counterfactual augmentation and sparse masks increase their effective training signal and reduce interference from large domains.
  • Knowledge transfer between domains is learned adaptively rather than assumed; the mask for each domain encodes which experts to use, so similar domains end up sharing experts and unrelated domains do not.
  • Maintenance cost drops relative to isolated per-domain models or pretraining-and-finetuning pipelines, because one shared model plus per-domain masks serves all domains.
  • The augmentation module is modular: the paper states that other suitable augmentation techniques could be substituted, making the mask-pruning core separable from the counterfactual data-generation choice.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the per-domain masks and their overlap ratios could be reused as a learned domain-similarity measure, potentially replacing the loss-distribution pre-clustering step that AREAD avoids.
  • Editorial inference: because the augmentation component is swappable, an ablation that replaces counterfactual augmentation with a non-counterfactual oversampling method would isolate how much of the minor-domain gain is due to the mask search alone.
  • Editorial inference: the same hierarchical mask-pruning search could be carried over to other multi-task settings with dozens of tasks, such as ranking across hundreds of scenarios, wherever an exhaustive search over task groupings is infeasible.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes AREAD, a multi-domain recommendation framework designed for dozens of domains. AREAD combines (i) a hierarchical expert integration (HEI) module, which uses a small number of experts at coarser layers and more, narrower experts at finer layers; (ii) a hierarchical expert mask pruning (HEMP) procedure inspired by the lottery ticket hypothesis, which learns a binary expert-selection mask for each domain; and (iii) a popularity-based counterfactual augmenter that copies positive interactions on unpopular items from major to minor domains. Experiments on Amazon (25 domains) and AliCCP (30 domains) report consistent AUC improvements over several single-domain and multi-domain baselines, with particular gains on minor-domain metrics. The paper also includes an ablation study, a hyperparameter study, and an analysis of learned mask overlap across domains.

Significance. If the reported results hold, AREAD addresses a real scalability gap in multi-domain recommendation, where most prior work considers only a handful of domains. The hierarchical mask-pruning idea is a plausible way to avoid per-domain towers while preserving domain-specific transfer patterns, and the public code release strengthens reproducibility. The two datasets with over twenty domains each are appropriate for the claimed setting. However, the significance is currently tempered by an experimental comparison that may handicap the baselines by construction, and by a counterfactual augmentation rule whose core transfer assumption is not directly validated. The reported gains, though statistically flagged, are small in absolute terms and are presented without variance information.

major comments (4)
  1. [Experimental Setup, 'Pre-Clustering Domains'] The comparison in Table 2 does not provide a neutral test of the claimed state-of-the-art performance. The paper states that, except for ADL and MAMDR, all multi-domain baselines are pre-clustered into 3 pseudo-domains, with multi-domain learning conducted 'within each cluster as a single domain.' This means MMoE, PLE, STAR, HiNet, EPNet, and PEPNet receive only a 3-valued cluster indicator instead of the original 25 or 30 domain IDs, while AREAD uses the original domain IDs in its hierarchical masks. The paper itself argues in Challenge 2 that pre-clustering 'overlooks intra-cluster domain variations,' and the Mask Analysis section gives a concrete example (Home & Kitchen vs. Appliances) where the learned masks overlap substantially while the pre-clustering may separate them. Thus the gains in Table 2 could partly reflect an information advantage rather than architectural superiority. The authors should either run the multi-domain baselines with the original domain IDs, or justify that the 3-cluster setup is the standard or strongest configuration for those baselines in this setting.
  2. [Popularity-based Counterfactual Augmenter, Corollary 1 (Eq. 5)] The load-bearing transfer assumption is not validated. Corollary 1 asserts that if a user positively interacts with an unpopular item in a major domain, the same interaction is likely in a minor domain. This assumption is used to generate augmented data for minor domains, and the paper attributes part of the minor-domain gains to it. However, no user-level evidence, no manual inspection, and no isolated ablation of the augmentation component is provided. The ablation in Figure 6 compares the full AREAD against a version without augmentation, but this conflates the augmentation effect with possible interactions between augmentation and mask pruning. The authors should provide a direct validation of the transfer assumption (e.g., measuring how often such cross-domain interest transfer actually occurs in the data) or at least a detailed sensitivity analysis showing that the augmentation does not inject noise that happens to help only the reported metrics.
  3. [Table 2 and 'Overall Performance'] The statistical reporting is incomplete for the scale of the claimed improvements. Results are averaged over five runs, but no standard deviations, confidence intervals, or per-run values are reported. The significance test is described only as 'p-value < 0.01' without stating which test was used, whether it accounts for multiple comparisons across many metrics and baselines, or whether it is paired per domain. Several reported improvements are small (2 permille on Amazon DomainAUC, 3 permille on Amazon Minor10AUC), and not all metrics carry the significance star. The authors should report variances and a precise description of the significance testing procedure so that the reader can assess robustness.
  4. [Ablation Study and Figure 6] The ablation shows that adding HEI alone slightly decreases performance relative to the MMoE base recommender, and that HEMP and the counterfactual augmenter each add gains. This is informative, but the figure does not report error bars or statistical significance for the incremental steps. Given the small absolute differences in Figure 6 (e.g., around 1-3 permille on several metrics), the authors should indicate whether these ablation differences are stable across the five runs and whether the improvement from the counterfactual augmenter is significant on minor-domain metrics when considered in isolation.
minor comments (4)
  1. [Overall Performance, observation (2)] The phrase 'closely variability the results of the Isolated method' appears to be a typo; it should likely read 'closely approaches the results' or similar.
  2. [Figure 7, hyperparameter study] The hyperparameter study reports only the Amazon dataset. Since AliCCP has a much more imbalanced domain distribution and different augmentation behavior, a short discussion of whether the same hyperparameter choices transfer to AliCCP would strengthen the paper.
  3. [Metrics, 'Minor10AUC' and 'Minor5AUC'] The definition of Minor10AUC and Minor5AUC is given only as 'the weighted average AUCs of the largest and smallest domains, respectively.' The sentence seems to contain a slip: it should specify that Minor10AUC is the weighted average AUC of the 10 smallest domains, not the largest. Please clarify.
  4. [Related Work] The related work section is useful but does not discuss recent works on multi-domain recommendation that use more than a handful of domains without pre-clustering, aside from MAMDR and ADL. A brief positioning against those approaches would help the reader understand the novelty boundary.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: AREAD's central claims rest on external benchmark evaluations and explicitly stated assumptions, not on equations that reduce to their own inputs.

full rationale

The paper's load-bearing claims are empirical evaluations on held-out test splits of two public datasets, with the method's components (hierarchical experts, mask pruning, counterfactual augmentation) specified as constructive procedures rather than as quantities fitted from the target metrics. The counterfactual Corollary 1 is explicitly derived from stated Assumptions 1 and 2, and it is used to synthesize training data for minor domains; it is not a prediction recovered from the test labels, so any observed improvement is conditional on the assumption holding rather than guaranteed by construction. Mask selection in Algorithm 1 chooses candidate masks by performance on training data, but the reported Table 2 metrics are computed on test data, making this ordinary model selection rather than a fitted constant renamed as a prediction. Self-citations such as Ying et al. 2023 and Chen et al. 2024a,b are used only as inspiration for counterfactual augmentation and are not invoked to justify the state-of-the-art claim. The possible unfairness of pre-clustering several multi-domain baselines into three pseudo-domains is a comparison-validity caveat, not a circularity, because AREAD's reported improvements are not algebraically entailed by the baseline setup. No step in the paper equates an output with an input by definition; therefore the circularity score is 0.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The central claims rest on the lottery ticket hypothesis, two counterfactual assumptions, and hand-tuned hyperparameters; no formal proof is given. The counterfactual augmentation assumptions are especially load-bearing for the minor-domain performance claims.

free parameters (6)
  • Mask sparsity and pruning schedule (S0, S, alpha) = S0=0.7, S=0.4, alpha=0.05
    Hand-chosen hyperparameters that control how aggressively per-domain expert masks are pruned; directly shape the learned transfer patterns.
  • Augmentation ratio raug = 0.10
    Chosen from the hyperparameter study on Amazon; sets how much counterfactual data is added to minor domains and is central to the reported minor-domain gains.
  • HEI architecture (L, experts per layer, hidden sizes) = L=3; [3, 6, 12] experts; hidden dims [(64,32), (32,16), (16,8)]
    Selected via hyperparameter exploration; determines model capacity and the granularity of shared knowledge.
  • Mask update schedule (update interval, candidate masks Z, batches k) = update every 2000 batches, Z=10, k=5
    Hand-set values that balance search cost and mask stability.
  • Updating learning rate lru = 0.01
    Tuned to the optimum in the hyperparameter study; controls how quickly candidate masks adapt.
  • Popularity threshold rho = not reported
    Defines 'unpopular item' for the counterfactual augmentation, but its numerical value is not given in the paper.
assumptions (3)
  • domain assumption Lottery Ticket Hypothesis: randomly initialized dense networks contain sparse subnetworks that, trained in isolation, can match the full network's accuracy.
    Invoked in 'Hierarchical Expert Mask Pruning' to justify the existence of good per-domain masks and the iterative pruning search. It is an empirical hypothesis, not proven for this architecture.
  • domain assumption Assumption 1: a positive interaction with an unpopular item is likely due to genuine interest rather than conformity (collider or explain-away effect).
    Stated in 'Popularity-based Counterfactual Augmentation'; underpins the decision to treat unpopular-item interactions as transferable signals.
  • domain assumption Assumption 2: a user's genuine interests are consistent across domains.
    Stated before Corollary 1; this is what licenses copying major-domain unpopular-item interactions into minor domains. No user-level validation is provided.

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Cite this review

Pith. "Pith review of One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation." pith.science (2026). https://pith.science/paper/GI4325QY

@misc{pith2026241211905,
  author       = {Pith},
  title        = {Pith review of: One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GI4325QY}},
  note         = {Machine review of arXiv:2412.11905}
}
read the original abstract

Multi-domain recommendation (MDR) aims to enhance recommendation performance across various domains. However, real-world recommender systems in online platforms often need to handle dozens or even hundreds of domains, far exceeding the capabilities of traditional MDR algorithms, which typically focus on fewer than five domains. Key challenges include a substantial increase in parameter count, high maintenance costs, and intricate knowledge transfer patterns across domains. Furthermore, minor domains often suffer from data sparsity, leading to inadequate training in classical methods. To address these issues, we propose Adaptive REcommendation for All Domains with counterfactual augmentation (AREAD). AREAD employs a hierarchical structure with a limited number of expert networks at several layers, to effectively capture domain knowledge at different granularities. To adaptively capture the knowledge transfer pattern across domains, we generate and iteratively prune a hierarchical expert network selection mask for each domain during training. Additionally, counterfactual assumptions are used to augment data in minor domains, supporting their iterative mask pruning. Our experiments on two public datasets, each encompassing over twenty domains, demonstrate AREAD's effectiveness, especially in data-sparse domains. Source code is available at https://github.com/Chrissie-Law/AREAD-Multi-Domain-Recommendation.

Figures

Figures reproduced from arXiv: 2412.11905 by the authors.

Figure 1
Figure 1. Sample size across 25 Amazon dataset domains, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Hierarchical Expert Mask Pruning (HEMP) gener [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Popularity-based Counterfactual Augmenter uti [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Comparison of performance, storage space, and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 7
Figure 7. Figure 7: Hyperparameter study on Amazon dataset. number of domains, leading to substantial enhancements in overall recommendation performance across all domains. (2) Our model delivers outstanding results at accept￾able maintenance costs. On the Amazon dataset, AREAD excels in …
Figure 8
Figure 8. Figure 8: Expert utilization in HEI of AREAD, with the left [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

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Forward citations

Cited by 2 Pith papers

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Reference graph

Works this paper leans on

44 extracted references · 35 canonical work pages · cited by 2 Pith papers

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Bai, G.; and Zhao, L. 2022. Saliency-regularized deep multi-task learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 15--25

    work page 2022

  4. [4]

    Chang, J.; Zhang, C.; Hui, Y.; Leng, D.; Niu, Y.; Song, Y.; and Gai, K. 2023. Pepnet: Parameter and embedding personalized network for infusing with personalized prior information. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3795--3804

    work page 2023

  5. [5]

    Chen, W.; Wu, Y.; Zhang, Z.; Zhuang, F.; He, Z.; Xie, R.; and Xia, F. 2024 a . FairGap: Fairness-aware Recommendation via Generating Counterfactual Graph. ACM Transactions on Information Systems, 42(4): 1--25

    work page 2024

  6. [6]

    Chen, W.; Yuan, M.; Zhang, Z.; Xie, R.; Zhuang, F.; Wang, D.; and Liu, R. 2024 b . FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning. arXiv preprint arXiv:2410.17555

    arXiv 2024

  7. [7]

    Fawcett, T. 2006. An introduction to ROC analysis. Pattern recognition letters, 27(8): 861--874

    2006

  8. [8]

    Frankle, J.; and Carbin, M. 2019. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. In International Conference on Learning Representations

    work page 2019

Show all 44 references
  1. [9]

    Gan, C.; Huang, B.; Hu, B.; Ma, J.; Zhang, Z.; Zhou, J.; Zhang, G.; and Zhong, W. 2024. PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 228--237

    2024

  2. [10]

    Gu, Y.; Bao, W.; Ou, D.; Li, X.; Cui, B.; Ma, B.; Huang, H.; Liu, Q.; and Zeng, X. 2021. Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce. In Proceedings of the 30th ACM International Conference on Information & Knowledge Manage...

    2021

  3. [11]

    Guan, R.; Pang, H.; Giunchiglia, F.; Liang, Y.; and Feng, X. 2022. Cross-Domain Meta-Learner for Cold-Start Recommendation. IEEE Transactions on Knowledge and Data Engineering

    2022

  4. [12]

    Guo, H.; Tang, R.; Ye, Y.; Li, Z.; and He, X. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, 1725--1731

    2017

  5. [13]

    Jia, P.; Wang, Y.; Lin, S.; Li, X.; Zhao, X.; Guo, H.; and Tang, R. 2024. D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, 8553--8561

    2024

  6. [14]

    P.; and Ba, J

    Kingma, D. P.; and Ba, J. 2015. Adam: A Method for Stochastic Optimization. In Bengio, Y.; and LeCun, Y., eds., 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings

    2015

  7. [15]

    Kuncheva, L. I. 2014. Combining pattern classifiers: methods and algorithms. John Wiley & Sons

    2014

  8. [16]

    Li, J.; Zheng, H.; Liu, Y.; Lu, M.; Wu, L.; and Hu, H. 2023. ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23, 1786–179...

    2023

  9. [17]

    Liu, Q.; Zhou, Z.; Jiang, G.; Ge, T.; and Lian, D. 2023. Deep Task-specific Bottom Representation Network for Multi-Task Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 1637--1646

    2023

  10. [18]

    Luo, L.; Li, Y.; Gao, B.; Tang, S.; Wang, S.; Li, J.; Zhu, T.; Liu, J.; Li, Z.; and Pan, S. 2023. MAMDR: A model agnostic learning framework for multi-domain recommendation. In 2023 IEEE 39th International Conference on Data Engineering (ICDE), 3079--3092. IEEE

    2023

  11. [19]

    Ma, J.; Zhao, Z.; Yi, X.; Chen, J.; Hong, L.; and Chi, E. H. 2018 a . Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 1930--1939

    2018

  12. [20]

    Ma, X.; Zhao, L.; Huang, G.; Wang, Z.; Hu, Z.; Zhu, X.; and Gai, K. 2018 b . Entire space multi-task model: An effective approach for estimating post-click conversion rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 1137--1140

    2018

  13. [21]

    MacQueen, J.; et al. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, 281--297. Oakland, CA, USA

    1967

  14. [22]

    Ni, J.; Li, J.; and McAuley, J. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language p...

    2019

  15. [23]

    Pearl, J.; and Mackenzie, D. 2018. The book of why: the new science of cause and effect. Basic books

    2018

  16. [24]

    Shen, Q.; Tao, W.; Zhang, J.; Wen, H.; Chen, Z.; and Lu, Q. 2021. Sar-net: a scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 4094--4103

    2021

  17. [25]

    Sheng, X.-R.; Zhao, L.; Zhou, G.; Ding, X.; Dai, B.; Luo, Q.; Yang, S.; Lv, J.; Zhang, C.; Deng, H.; et al. 2021. One model to serve all: Star topology adaptive recommender for multi-domain ctr prediction. In Proceedings of the 30th ACM International Conference on Information ...

    2021

  18. [26]

    Song, W.; Shi, C.; Xiao, Z.; Duan, Z.; Xu, Y.; Zhang, M.; and Tang, J. 2019. Autoint: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of the 28th ACM international conference on information and knowledge management, 1161--1170

    2019

  19. [27]

    Standley, T.; Zamir, A.; Chen, D.; Guibas, L.; Malik, J.; and Savarese, S. 2020. Which tasks should be learned together in multi-task learning? In International Conference on Machine Learning, 9120--9132. PMLR

    2020

  20. [28]

    Sun, T.; Shao, Y.; Li, X.; Liu, P.; Yan, H.; Qiu, X.; and Huang, X. 2020. Learning sparse sharing architectures for multiple tasks. In Proceedings of the AAAI conference on artificial intelligence, volume 34, 8936--8943

    2020

  21. [29]

    Tang, H.; Liu, J.; Zhao, M.; and Gong, X. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems, 269--278

    2020

  22. [30]

    Wang, C.; Senjie; Shuli; Wen, S.; Yin, H.; and Xiao, X. 2023. Exploration and Practice of Multi-Scenario Modeling at Meituan. Technical report, Meituan. Accessed: 2024/02/01

    2023

  23. [31]

    Wang, R.; Fu, B.; Fu, G.; and Wang, M. 2017. Deep & Cross Network for Ad Click Predictions. In Proceedings of the ADKDD'17, ADKDD'17. New York, NY, USA: Association for Computing Machinery. ISBN 9781450351942

    2017

  24. [32]

    Wang, R.; Shivanna, R.; Cheng, D.; Jain, S.; Lin, D.; Hong, L.; and Chi, E. 2021. Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the web conference 2021, 1785--1797

    2021

  25. [33]

    Wang, Y.; Guo, H.; Chen, B.; Liu, W.; Liu, Z.; Zhang, Q.; He, Z.; Zheng, H.; Yao, W.; Zhang, M.; et al. 2022. Causalint: Causal inspired intervention for multi-scenario recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4090--4099

    2022

  26. [34]

    Yan, B.; Wang, P.; Zhang, K.; Li, F.; Deng, H.; Xu, J.; and Zheng, B. 2022. Apg: Adaptive parameter generation network for click-through rate prediction. Advances in Neural Information Processing Systems, 35: 24740--24752

    2022

  27. [35]

    Yang, X.; Peng, X.; Wei, P.; Liu, S.; Wang, L.; and Zheng, B. 2022. Adasparse: Learning adaptively sparse structures for multi-domain click-through rate prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 4635--4639

    2022

  28. [36]

    Ying, Y.; Zhuang, F.; Zhu, Y.; Wang, D.; and Zheng, H. 2023. Camus: attribute-aware counterfactual augmentation for minority users in recommendation. In Proceedings of the ACM Web Conference 2023, 1396--1404

    2023

  29. [37]

    Zhang, Q.; Liao, X.; Liu, Q.; Xu, J.; and Zheng, B. 2022 a . Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM '22, 1368–1376. New ...

    2022

  30. [38]

    Zhang, W.; Zhang, P.; Zhang, B.; Wang, X.; and Wang, D. 2023. A Collaborative Transfer Learning Framework for Cross-domain Recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5576--5585

    2023

  31. [39]

    Zhang, Y.; Wang, X.; Hu, J.; Gao, K.; Lei, C.; and Fang, F. 2022 b . Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 3674--3683

    2022

  32. [40]

    Zhao, C.; Zhao, H.; He, M.; Zhang, J.; and Fan, J. 2023. Cross-domain recommendation via user interest alignment. In Proceedings of the ACM Web Conference 2023, 887--896

    2023

  33. [41]

    Zheng, Y.; Gao, C.; Li, X.; He, X.; Li, Y.; and Jin, D. 2021. Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the Web Conference 2021, 2980--2991

    2021

  34. [42]

    Zhou, J.; Cao, X.; Li, W.; Bo, L.; Zhang, K.; Luo, C.; and Yu, Q. 2023. Hinet: Novel multi-scenario & multi-task learning with hierarchical information extraction. In 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2969--2975. IEEE

    2023

  35. [43]

    Zhu, Y.; Ge, K.; Zhuang, F.; Xie, R.; Xi, D.; Zhang, X.; Lin, L.; and He, Q. 2021. Transfer-meta framework for cross-domain recommendation to cold-start users. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1...

    2021

  36. [44]

    Zou, X.; Hu, Z.; Zhao, Y.; Ding, X.; Liu, Z.; Li, C.; and Sun, A. 2022. Automatic expert selection for multi-scenario and multi-task search. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1535--1544

    2022

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.