Modeling User Behavior from Adaptive Surveys with Supplemental Context

Aman Shukla; Daniel Patrick Scantlebury; Rishabh Kumar

REVIEW 3 major objections 6 minor 14 references

Modeling User Behavior from Adaptive Surveys with Supplemental Context

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A survey-first fusion architecture called LANTERN raises multi-label survey-response prediction F1 from 0.734 to 0.775 by gating external context through cross-attention.

desk verdict Sensible fusion blueprint for survey-centric modeling, but the missing input-target split makes the headline comparison uninterpretable until the authors clarify it. read the letter →

arxiv 2507.20919 v1 pith:O5B5VKF3 submitted 2025-07-28 cs.LG cs.AIcs.IR

Aman Shukla , Daniel Patrick Scantlebury , Rishabh Kumar This is my paper · ORCID

classification cs.LGcs.AIcs.IR

keywords adaptivesurveysmulti-labelpredictioncross-attentionlatefusiongatedresidualconnectionsuserbehaviormodelingtabulardatasurveyprimacy

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

This paper argues that when survey responses and passively collected behavioral data are combined, the survey should remain the anchor signal and external context should be admitted selectively. LANTERN encodes the two signal types separately, lets the survey embedding query the external context through cross-attention, and fuses the result with a learned gate plus residual connection. On a dataset of roughly 35,000 users, LANTERN reaches 0.775 F1 for multi-label prediction of individual response keys, compared with 0.734 for the survey-only baseline; most of the gain is a recall increase. A learned gate that clusters near zero with some values near one supports the paper's premise that external signals are noisy and should be used only when relevant. The claim matters because it offers a practical, modular template for behavior modeling where rich surveys are the ground truth.

What carries the argument

The load-bearing object is the gated residual cross-attention fusion module: $h_t = \mathrm{Encoder}(Q=h_s, K=h_e, V=h_e)$, then $h_{\mathrm{fused}} = h_s + g \odot (h_t - h_s)$ with $g\in(0,1)$ learned. The survey embedding acts as the query; the external embedding supplies key and value, so the model attends only to external context that the survey deems relevant. The gate decides how much of the attended difference to add, and the residual keeps the survey representation as the default. A mask $m\in\{-1,0,1\}$ over response keys tells the loss to count only favorable or non-favorable answers and ignore keys the adaptive survey did not ask.

What would settle it

Run the same ablation on the same 35,000-user dataset with an explicit leak-free feature/target split and a corrected loss mask; if a survey-only model then matches LANTERN's F1 within measurement error, the architecture's contribution is falsified.

Watch

Extended reading notes

Core claim

The central discovery is that a fused model can beat a strong survey-only model without letting noisy external context dominate: the survey embedding is used as the query in a transformer block over external embeddings, and the output is combined with the survey embedding via $h_{\mathrm{fused}} = h_s + g \odot (h_t - h_s)$, with $g$ learned per instance and dimension. Each answer option is treated as an independent binary label, and a mask excludes options the user was not asked. In the reported ablation, LANTERN reaches precision 0.8263, recall 0.7296, and F1 0.7750, while survey-only is 0.7976/0.6794/0.7338 and external-only is 0.7537/0.4264/0.5447. The paper reads the recall gain as evidence that supplemental context recovers selections the survey did not explicitly query, especially on rare attributes, where LANTERN's F1 is 0.8575 versus 0.8448 survey-only.

Load-bearing premise

The comparison stands on the assumption that the survey features fed to the encoders do not already contain the response keys being predicted; the paper does not describe how this disjoint split is constructed.

Editorial extensions

If this is right

If LANTERN's result holds, survey-only prediction is not the ceiling: the five-point recall gain is attainable without sacrificing precision, since precision actually rises from 0.7976 to 0.8263.
The gate's learned bimodal behavior means the model can behave almost like a survey-only model on some users and defer to external data on others, which gives a built-in fail-safe when external signals are missing or misaligned.
Because the encoders are decoupled and late fusion happens after per-modality encoding, new data sources can be added without retraining the survey encoder, and inference degrades gracefully when external data is absent.
When the adaptive survey's label set changes, the paper's synchronous-retraining strategy keeps the output head aligned with the current response keys, avoiding predictions for deprecated questions.
The relatively flat F1 across thresholds suggests the model's probabilities are usable at multiple operating points, which supports deployment choices that trade precision for recall per response key.

Reading between the lines

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

Editorial inference: the paper does not directly test whether the gate actively suppresses corrupted external features; a natural follow-up is to corrupt or shuffle a fraction of external columns and check whether F1 drops far less for LANTERN than for an ungated late-fusion model.
Editorial inference: since the recall gain is largest on rare attributes, the mechanism may be recovering conditionally shown options from demographic or behavioral correlates; one testable extension is to measure per-key recall gains against the question's conditional display rule.
Editorial inference: the survey-primacy design suggests a cheaper alternative to collecting longer surveys: keep the survey short and let gated external context fill coverage gaps; the paper's numbers imply this could work, but only if the features and targets are genuinely disjoint.
Editorial inference: the bimodal gate distribution hints at a discrete 'use external or not' decision; converting the continuous gate into a hard switch during deployment could make inference cheaper and the model more auditable, at some small cost in F1.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a reasonable engineering paper on fusing survey responses with external context for user behavior prediction. The architecture—late fusion via cross-attention with a learned gate that keeps survey embeddings primary—is a sensible combination of known pieces, and the gating analysis in Appendix B is genuinely interesting. What sinks the current version is not the model but the evaluation geometry. Section 3.1 defines both the input xs and the target y in terms of the same adaptive survey response keys, and the paper never states that the target keys are held out of xs. If they are not, the survey-only baseline can copy answers from its input, and the reported F1 of 0.734 for survey-only and 0.775 for LANTERN (Table 1) may reflect input-label leakage rather than the gated cross-attention contribution. That is the load-bearing question and it has to be resolved before the numbers can be believed.

The loss written in Section 3.2.4 as BinaryCrossentropy(m⊙ŷ) with m∈{-1,0,1} is not valid standard BCE. The likely intent is a masked BCE that drops the m=0 entries and maps -1 to the negative class, but as printed the equation is wrong. This is fixable, but it adds to the impression that the experimental details were written hastily. There are no error bars, no significance tests, and no code or data, so the single-table result carries limited information even after the split is clarified.

On the positive side, the paper clearly explains the production setting, the modular encoders are a real plus, and Appendix C's discussion of label-space drift across survey cycles is more thoughtful than most workshop papers get. The reported gain (roughly +0.04 F1, with a 5-point recall lift) is modest, which makes the circularity concern more damning rather than less.

My take: if the split is resolved and the loss fixed, this is a usable blueprint for people building survey-centric behavior models in industry. As it stands, it is not yet a reliable empirical result. I would not cite it in my own work until the authors clarify the data split and ideally add some variance estimate. I would bring it to a reading group as a case study in how easy it is to leave label leakage ambiguous in a paper, and I would send it to peer review if the venue expects a revision cycle—the architecture and the production framing deserve referee time, but the authors need to show the split explicitly.

Referee Report

3 major / 6 minor

Summary. The paper proposes LANTERN, a modular architecture for multi-label prediction of adaptive survey responses by fusing survey features with external contextual features through a gated cross-attention mechanism. The empirical study on about 35,000 users reports that LANTERN achieves F1=0.775 versus survey-only F1=0.734 and external-only F1=0.545, with additional rare/frequent attribute and threshold analyses. The central claim is that preserving survey primacy via selective gating and late cross-attention fusion yields better response-key predictions than survey-only or external-only baselines.

Significance. If the comparison is valid, the paper offers a useful industrial case study and a modular architecture for survey-centric behavior modeling, with notable strengths including a production deployment, a learned-gating analysis, and a threshold-sensitivity diagnostic. However, the validity of the central claim currently hinges on unverified disjointness between the survey input features and the target response keys, and on a loss formulation that is not mathematically well defined as written. The significance of the contribution therefore cannot be assessed until these load-bearing issues are resolved.

major comments (3)

[Section 3.1 / Section 4.0.2] The paper does not state that the response keys in the target matrix y are held out from the survey feature matrix xs. Since xs is defined as "adaptive survey responses" and y as the matrix of "distinct response keys," a survey-only model may be able to copy the ground-truth labels from its own input, which would make the survey-only baseline in Table 1 circular and would invalidate the comparison with LANTERN. Please specify exactly which features enter xs, describe the train/validation/test split over users and response keys, and, if the intended task is held-out-key prediction, document the protocol in Section 4.0.2.
[Section 3.2.4, Eq. (1)] The loss written as L = BinaryCrossentropy(m ⊙ ŷ) is not a valid binary cross-entropy: the target y is absent, and multiplying sigmoid predictions by mask entries in {-1, 0, 1} yields negative values inside the log. The mask semantics described in Appendix A suggest an ignore/weight scheme over a per-key BCE term, not a direct product with the prediction. Please give the precise per-key loss involving y, the mask, and the handling of "not asked" keys, and state how "non-favorable" labels are defined for response keys.
[Table 1 / Section 5.2] The central empirical claim is based on a single ablation table with no variance estimates, no repeated-seed runs, and no significance tests; the LANTERN-versus-survey-only F1 gap is 0.775 vs 0.734, which could be within run-to-run noise. Please report mean and standard deviation over multiple seeds, the decision threshold used for the metrics, and, if possible, a paired significance test over users or response keys.

minor comments (6)

[Section 4.0.2] The phrase "it's performance" should be "its performance."
[Section 3.2.4] The output dimension is written as "N×d s" in several places; use N×d for clarity.
[Section 3.2.3] The symbol G used for Gaussian noise conflicts with the gate variable g; rename one of them to avoid ambiguity.
[Appendix A] The phrase "to 512 for repetition" is unclear; it should probably say "down-projected back to 512 for the next block."
[Figure 3] Thresholds at 0.3, 0.5, and 0.7 are only three points; the text claims a "relatively flat F1 curve," but more threshold values would make this diagnostic more convincing.
[Appendix A / Section 4.0.1] No public code or data is provided; if the data are proprietary, please at least document the names, types, and preprocessing of the external features in an appendix to support reproducibility.

Circularity Check

2 steps flagged · score 6.0 of 10

Central comparison is potentially self-definitional: xs (survey responses) and y (response-key labels) are not defined as disjoint, so survey-only and LANTERN may read labels from the input; the printed loss further ties supervision to the masked answer pattern.

self definitional [Section 3.1 (Problem Formulation) and Table 1]
"Let xs denote adaptive survey responses ... We model this at the response key level: each possible answer option is treated as an independent binary label. ... Let y ∈ {0,1}^{N×d} be the multi-label target matrix, where d is the number of distinct response keys."

The input xs is defined as the adaptive survey responses and y is defined as the binary label matrix over the distinct response keys of those same responses. No sentence in Section 3.1, Section 3.2.4, or Section 4 states that the target response keys are withheld from xs. Under the paper's own definitions, fs(xs) has direct access to the answer pattern that y encodes, so a survey-only model can solve the multi-label task by copying. The reported F1 values (survey-only 0.7338, LANTERN 0.7750) therefore do not establish that late-fusion cross-attention adds predictive information; they may only confirm input-label overlap. The external-only row (0.5447) is an independent control, but the central survey-primary claim is not independently testable as described.
other [Section 3.2.4 and Appendix A (Output Head & Loss / Output Label and Mask)]
"L = BinaryCrossentropy(m⊙ŷ) ... A mask m∈ {−1,0,1} depending on whether the response key is non-favorable, wasn't asked, or favorable respectively is applied. This masking is done to calibrate the loss function and inform learning from ground truth (favorable/non-favorable) only."

The loss as written contains no reference to y; the mask m itself encodes the favorable/non-favorable ground truth and is applied to the predictions. Thus the supervision is the same response-key pattern that is already present in xs under the Section 3.1 definitions. The printed BCE formula is also not a standard binary cross-entropy against a {0,1} target, so the paper does not clarify how labels are separated from inputs. This makes the input/target overlap a property of the described training procedure rather than a resolved experimental detail.

full rationale

The paper contains no self-citations and no imported uniqueness theorems, so the circularity does not come from the citation chain. Instead, the central claim—that LANTERN's gated cross-attention fusion beats survey-only baselines in multi-label prediction of survey responses—is compromised by the problem formulation itself. Section 3.1 defines the input as adaptive survey responses and the target as the binary matrix over the distinct response keys of those responses, without ever specifying a holdout that removes the target keys from the survey input. Under the stated definitions, a survey-only encoder can read the answers it is asked to predict, making the comparison in Table 1 a potential test of input-label overlap rather than of architectural value. The external-only baseline provides some independent grounding, which is why the score is not higher, but the survey-primary claim is the paper's headline result and it is not established as written. The loss equation in Section 3.2.4 and the mask description in Appendix A reinforce the confusion by using the mask itself as the effective target. Overall, this is a partial, construction-level circularity in the central evaluation, warranting a score of 6.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no explicit axioms or new physical entities. The central claim rests on the domain assumptions that survey responses are trustworthy labels, that external data can be aligned without leakage, and that the undefined train/test split avoids circularity. Hand-chosen quantities are limited to architectural hyperparameters and the implicit decision threshold.

free parameters (2)

Decision threshold for reported metrics = 0.5 (implied; also evaluates 0.3 and 0.7)
The main F1 scores in Tables 1 and 2 do not state which threshold was used; the thresholding analysis in Figure 3 shows sensitivity, so the threshold is a hand-chosen operating point.
Architecture hyperparameters (3 transformer layers, 8 heads, embedding dims 512/2048, hidden 4096, learning rate 1e-3… = As listed in Appendix A
Selected via preliminary experiments and standard practice; no sensitivity analysis is provided beyond the threshold study, so these are effectively chosen by hand.

assumptions (3)

domain assumption Survey responses are an informative, high-fidelity ground truth for user behavior
The paper treats self-reported answers as reliable labels, despite acknowledging fatigue, incomplete responses, and voluntary-response bias.
domain assumption External data is aligned to users without leakage from the target response keys
The paper states external data is 'aligned at the user level' (Section 4) but gives no details on how this alignment avoids information about held-out survey answers.
domain assumption The response-key masking procedure correctly separates input features from target labels
The loss uses a mask m in {-1,0,1}; the relationship between xs and y is never specified, so the evaluation may be circular if the survey encoder sees the keys being predicted.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Modeling User Behavior from Adaptive Surveys with Supplemental Context." pith.science (2026). https://pith.science/paper/O5B5VKF3

@misc{pith2026250720919,
  author       = {Pith},
  title        = {Pith review of: Modeling User Behavior from Adaptive Surveys with Supplemental Context},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O5B5VKF3}},
  note         = {Machine review of arXiv:2507.20919}
}

read the original abstract

Modeling user behavior is critical across many industries where understanding preferences, intent, or decisions informs personalization, targeting, and strategic outcomes. Surveys have long served as a classical mechanism for collecting such behavioral data due to their interpretability, structure, and ease of deployment. However, surveys alone are inherently limited by user fatigue, incomplete responses, and practical constraints on their length making them insufficient for capturing user behavior. In this work, we present LANTERN (Late-Attentive Network for Enriched Response Modeling), a modular architecture for modeling user behavior by fusing adaptive survey responses with supplemental contextual signals. We demonstrate the architectural value of maintaining survey primacy through selective gating, residual connections and late fusion via cross-attention, treating survey data as the primary signal while incorporating external modalities only when relevant. LANTERN outperforms strong survey-only baselines in multi-label prediction of survey responses. We further investigate threshold sensitivity and the benefits of selective modality reliance through ablation and rare/frequent attribute analysis. LANTERN's modularity supports scalable integration of new encoders and evolving datasets. This work provides a practical and extensible blueprint for behavior modeling in survey-centric applications.

Figures

Figures reproduced from arXiv: 2507.20919 by the authors.

**Figure 1.** Ecosystem Overview. (Left) Pipeline for generating signal xs from survey. (Right) Pipeline to generate signal xe from external data. Both pipelines sample information from a database of survey respondents that includes different modalities including user behavior. 2. Related Work Recent advancements in deep learning for user modeling have demonstrated the utility of structured behavioral signals. For example, Sukh… view at source ↗

**Figure 2.** LANTERN with cross-attention and late fusion. 3.2. Architecture 3.2.1. ENCODER LAYERS Inputs xs ∈ RN×Fs and xe ∈ RN×Fe are encoded separately: hs N×D = fs(xs) he N×D = fe(xe) where Fs, Fe are features in the survey and external datasets. fs, fe are learned encoders for the survey and external data respectively. hs, he are corresponding embeddings of size N × D. 3.2.2. CROSS ATTENTION LAYER The encoded survey repres… view at source ↗

**Figure 3.** illustrates how LANTERN’s performance metrics vary across thresholds. As the threshold increases, precision improves steadily, while recall drops. The F1-score remains comparatively stable (∼0.75). These trends reflect the typical trade-offs in multi-label prediction: lower thresholds recover more positives (higher recall) but reduce confidence (lower precision), while higher thresholds reduce false positives at t… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Gating Behavior for 100 user samples: LANTERN learns to commit; indicated with minimal weights in the mid-range. Gate also treats survey-only as a strong anchor with most weights clustering towards 0 which prevents high-volume noisy external signal from dominating pred…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

14 extracted references · 6 canonical work pages

[4]

Gu, Y ., Ding, Z., Wang, S., Zou, L., Liu, Y ., and Yin, D

doi: 10.1109/TKDE.2019.2958808. Gu, Y ., Ding, Z., Wang, S., Zou, L., Liu, Y ., and Yin, D. Deep multifaceted transformers for multi-objective rank- ing in large-scale e-commerce recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM ’20, pp. 2493–2500, New York, NY , USA,

work page arXiv 2019
[5]

ISBN 9781450368599

Associa- tion for Computing Machinery. ISBN 9781450368599. doi: 10.1145/3340531.3412697. URL https://doi. org/10.1145/3340531.3412697. Gu, Y ., Bao, W., Ou, D., Li, X., Cui, B., Ma, B., Huang, H., Liu, Q., and Zeng, X. Self-supervised learning on users’ spontaneous behaviors for multi- scenario ranking in e-commerce. InProceedings of the 30th ACM Internat...

work page arXiv
[6]

ISBN 9781450384469

Association for Comput- ing Machinery. ISBN 9781450384469. doi: 10.1145/ 3459637.3481953. URL https://doi.org/10. 1145/3459637.3481953. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T. Neural collaborative filtering.CoRR, abs/1708.05031,

arXiv
[8]

5 Modeling User Behavior from Adaptive Surveys with Supplemental Context Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., and Chi, E. H. Modeling task relationships in multi-task learn- ing with multi-gate mixture-of-experts. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18, pp. 1930–1939, New York, ...

work page 1930
[10]

URL https://online-journals.org/index

doi: 10.3991/ijet.v14i19.10366. URL https://online-journals.org/index. php/i-jet/article/view/10366. Tsai, Y .-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., and Salakhutdinov, R. Multimodal transformer for unaligned multimodal language sequences. In Ko- rhonen, A., Traum, D., and M `arquez, L. (eds.),Pro- ceedings of the 57th Annual Meeting...

work page doi:10.3991/ijet.v14i19.10366
[11]

doi: 10.18653/v1/P19-1656

Association for Computa- tional Linguistics. doi: 10.18653/v1/P19-1656. URL https://aclanthology.org/P19-1656/. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. Attention is all you need. InProceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, pp. 600...

doi:10.18653/v1/p19-1656
[12]

ISBN 9781510860964

Curran Associates Inc. ISBN 9781510860964. Wei, W., Huang, C., Xia, L., and Zhang, C. Multi-modal self-supervised learning for recommendation. InPro- ceedings of the ACM Web Conference 2023, WWW ’23, pp. 790–800. ACM, April

work page 2023
[14]

Beyond Co-occurrence: Multi-modal Session-based Recommendation

URL https://arxiv.org/ abs/2309.17037. 6 Modeling User Behavior from Adaptive Surveys with Supplemental Context A. Implementation Details LANTERN is implemented in TensorFlow and trained using the Keras functional API. The model consists of two encoders, one for survey features, and one for external context followed by a transformer based fusion block, a ...

work page Pith review arXiv

Show all 14 references

[2017]

Lu, J., Batra, D., Parikh, D., and Lee, S.ViLBERT: pre- training task-agnostic visiolinguistic representations for vision-and-language tasks

URL http://arxiv.org/ abs/1708.05031. Lu, J., Batra, D., Parikh, D., and Lee, S.ViLBERT: pre- training task-agnostic visiolinguistic representations for vision-and-language tasks. Curran Associates Inc., Red Hook, NY , USA,

arXiv
[2018]

ISBN 9781450355520

Associa- tion for Computing Machinery. ISBN 9781450355520. doi: 10.1145/3219819.3220007. URL https://doi. org/10.1145/3219819.3220007. Sukhbaatar, O., Usagawa, T., and Choimaa, L. An artificial neural network based early prediction of failure-prone students in blended learning...
[2019]

org/abs/1810.04805

URL https://arxiv. org/abs/1810.04805. Gao, C., He, X., Gan, D., Chen, X., Feng, F., Li, Y ., Chua, T.- S., Yao, L., Song, Y ., and Jin, D. Learning to recommend with multiple cascading behaviors.IEEE Transactions on Knowledge and Data Engineering, 33(6):2588–2601,

arXiv
[2020]

doi: 10.1007/s00521-019-04559-1

ISSN 0941-0643. doi: 10.1007/s00521-019-04559-1. URL https://doi. org/10.1007/s00521-019-04559-1. Chen, C., Ma, W., Zhang, M., Wang, Z., He, X., Wang, C., Liu, Y ., and Ma, S. Graph heteroge- neous multi-relational recommendation.Proceedings of the AAAI Conference on Artificia...

doi
[2021]

doi: 10.1609/aaai.v35i5. 16515. URL https://ojs.aaai.org/index. php/AAAI/article/view/16515. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. Bert: Pre-training of deep bidirectional transformers for lan- guage understanding,

doi
[2023]

doi: 10.1145/3543507. 3583206. URL http://dx.doi.org/10.1145/ 3543507.3583206. Zhang, X., Xu, B., Ma, F., Li, C., Yang, L., and Lin, H. Beyond co-occurrence: Multi-modal session-based rec- ommendation,

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [4]

Gu, Y ., Ding, Z., Wang, S., Zou, L., Liu, Y ., and Yin, D

doi: 10.1109/TKDE.2019.2958808. Gu, Y ., Ding, Z., Wang, S., Zou, L., Liu, Y ., and Yin, D. Deep multifaceted transformers for multi-objective rank- ing in large-scale e-commerce recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM ’20, pp. 2493–2500, New York, NY , USA,

work page arXiv 2019

[2] [5]

ISBN 9781450368599

Associa- tion for Computing Machinery. ISBN 9781450368599. doi: 10.1145/3340531.3412697. URL https://doi. org/10.1145/3340531.3412697. Gu, Y ., Bao, W., Ou, D., Li, X., Cui, B., Ma, B., Huang, H., Liu, Q., and Zeng, X. Self-supervised learning on users’ spontaneous behaviors for multi- scenario ranking in e-commerce. InProceedings of the 30th ACM Internat...

work page arXiv

[3] [6]

ISBN 9781450384469

Association for Comput- ing Machinery. ISBN 9781450384469. doi: 10.1145/ 3459637.3481953. URL https://doi.org/10. 1145/3459637.3481953. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T. Neural collaborative filtering.CoRR, abs/1708.05031,

arXiv

[4] [8]

5 Modeling User Behavior from Adaptive Surveys with Supplemental Context Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., and Chi, E. H. Modeling task relationships in multi-task learn- ing with multi-gate mixture-of-experts. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18, pp. 1930–1939, New York, ...

work page 1930

[5] [10]

URL https://online-journals.org/index

doi: 10.3991/ijet.v14i19.10366. URL https://online-journals.org/index. php/i-jet/article/view/10366. Tsai, Y .-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., and Salakhutdinov, R. Multimodal transformer for unaligned multimodal language sequences. In Ko- rhonen, A., Traum, D., and M `arquez, L. (eds.),Pro- ceedings of the 57th Annual Meeting...

work page doi:10.3991/ijet.v14i19.10366

[6] [11]

doi: 10.18653/v1/P19-1656

Association for Computa- tional Linguistics. doi: 10.18653/v1/P19-1656. URL https://aclanthology.org/P19-1656/. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. Attention is all you need. InProceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, pp. 600...

doi:10.18653/v1/p19-1656

[7] [12]

ISBN 9781510860964

Curran Associates Inc. ISBN 9781510860964. Wei, W., Huang, C., Xia, L., and Zhang, C. Multi-modal self-supervised learning for recommendation. InPro- ceedings of the ACM Web Conference 2023, WWW ’23, pp. 790–800. ACM, April

work page 2023

[8] [14]

Beyond Co-occurrence: Multi-modal Session-based Recommendation

URL https://arxiv.org/ abs/2309.17037. 6 Modeling User Behavior from Adaptive Surveys with Supplemental Context A. Implementation Details LANTERN is implemented in TensorFlow and trained using the Keras functional API. The model consists of two encoders, one for survey features, and one for external context followed by a transformer based fusion block, a ...

work page Pith review arXiv