REVIEW 4 major objections 4 minor 54 references
ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors
T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read ShiJianBench claims that accurate, personalized, and compliant advisor responses do not necessarily produce proportionally stronger long-horizon investor outcomes, because advice works through hidden investor-state changes that only become
desk verdict A well-engineered, auditable framework for trajectory-level advisor evaluation, but the headline dissociation between response quality and long-horizon impact is only as strong as the unvalidated latent-state simulator behind it. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing object is a multi-agent investor simulator with an explicit, auditable latent state $z_t$ capturing beliefs, perceived risk, trust, affect, and attention. Four motive-driven sub-agents, Sentinel, Hedonist, Socialite, and Executive, deliberate and arbitrate each daily action; advisor dialogue updates belief components through a text-grounded update rule (Eq. 21); and long-term memory and reflection evolve slower traits. Advisor impact is measured through matched counterfactual rollouts, where the same investor initialization and market trace run under a no-advisor baseline and under the target advisor, then scored by investor-side, business-value, and content metrics with the
What would settle it
A prospective randomized study where retail investors receive advisor messages that score high versus low on the paper's content rubric, then are tracked for a year; if high content scores produce proportionally stronger risk-adjusted returns and lower drawdowns, the claimed systematic distinction between response quality and long-horizon intervention is contradicted.
Extended reading notes
Core claim
The central claim is stated directly in the abstract: accurate, personalized, and compliant responses do not necessarily produce proportionally stronger long-horizon investor outcomes, revealing a systematic distinction between producing a high-quality response and delivering an effective long-horizon intervention. The paper supports this with paired-uplift comparisons between response-level scores and trajectory-level scores under a hard compliance gate. DeepSeek V4 and Claude Sonnet 4.6 rank as the top two advisors in all three market regimes on the composite score, mainly through substantially stronger personalized content combined with competitive investor-side outcomes, while a determin
Load-bearing premise
The entire ranking rests on the simulator's internal belief-update equations faithfully capturing how real investors change their decisions after reading advisor messages, because only aggregate portfolio-risk structure and buy/sell elasticities were calibrated, not the latent mediation layer.
Editorial extensions
If this is right
- If the paper is right, benchmarks that grade advisor responses on accuracy, compliance, and personalization are measuring something different from long-horizon investor impact, so deployment decisions should use trajectory-level evidence.
- General-purpose frontier LLMs, rather than finance-tuned models, would be the strongest overall conversational advisors in this setting, because content quality contributes to the integrated score but must be paired with competitive trajectory outcomes.
- A deterministic personalized rule-based advisor remains a strong reference point on investor-side outcomes, implying that sophisticated LLM policy is not required for all long-horizon gains.
- Compliance must be treated as a hard gate: advice that changes behavior but crosses red lines is disqualified regardless of trajectory improvement.
- Regime-level stability of the top two advisors suggests the leading group's advantage is not confined to one market condition.
Reading between the lines
- Editorial extension: because calibration covers only aggregate portfolio-risk structure and buy/sell elasticities, the ranking is conditional on the unvalidated mediation layer; per-user longitudinal action data or a prospective trial would be needed to confirm that the text-grounded belief updates mirror real decision changes.
- Editorial extension: the same matched-counterfactual, explicit-latent-state design transfers to other domains where dialogue alters beliefs and behavior over time, such as health coaching, legal aid, or career guidance, with domain-specific compliance gates.
- Editorial extension: a cheap check of the mediation claim is to ablate the text-grounded belief update; if advisor rankings survive, the long-horizon effects are carried by something else in the simulator.
- Editorial extension: the paper's distinction implies that AI-advisor regulation should focus on downstream investor behavior and outcomes, not only on the wording of disclosures.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces SHIJIANBENCH, an offline benchmark for evaluating conversational investment advisors through long-horizon matched counterfactual trajectories under fixed historical Chinese fund-market data. A multi-agent investor simulator with explicit latent states (belief, risk perception, trust, affect, attention) is calibrated to aggregate statistics from 7,199 real users, and advisor policies are scored by investor-side, business-side, and dialogue-content metrics under a hard compliance gate. The headline empirical finding is that high content quality (accuracy/personalization/compliance) does not necessarily translate into proportionally stronger long-horizon investor outcomes, with DeepSeek V4 and Claude Sonnet 4.6 forming a stable leading group across market regimes. The central claim is therefore that response quality and long-horizon intervention quality are related but not interchangeable.
Significance. The proposed framework addresses a real gap: existing advisory benchmarks stop at response quality or aggregate outcomes and do not audit the dialogue-to-behavior pathway. The strengths of the paper are substantial: a matched counterfactual protocol with fixed market traces, a hard compliance gate with rubric-based judging, separation of calibration from advisor evaluation (80/20 user-disjoint split), independently recalibrated ablation variants, LLM-judge agreement checks against human annotations, and qualitative audit traces that make individual dialogue-outcome linkages inspectable. If the simulator's mediation layer were validated, the benchmark would be a valuable infrastructure contribution to trajectory-aware evaluation of conversational advisors. However, the central empirical claim is currently simulator-conditional: the link from advisor language to investor behavior is implemented by an unvalidated text-grounded latent-state update, and the reported advisor differences in investor-side outcomes are numerically tiny and lack inferential support. The framework is promising, but the paper's conclusions as stated go beyond what the experiments establish.
major comments (4)
- [§3.2 / Appendix C.4, Eq. (21)] The central claim—that accurate/personalized/compliant responses do not necessarily produce proportionally stronger long-horizon outcomes—is read off rollouts in which advisor text changes investor decisions only through the latent state z_t via Eq. (21): b_+^{(k)}_t = λ_b b^{(k)}_t + (1−λ_b) h_k(d_t,o_t). The text-grounded update h_k is a rule–LLM hybrid and is not among the calibration targets. Calibration (§4.2, Appendix J) matches only the RiskIndex distribution and buy/sell elasticities computed from market/portfolio outcomes; dialogue realism is scored post-hoc by a user-side LLM judge on utterance style and consistency, which does not test whether real investors' post-dialogue decisions change as the simulator predicts. The dissociation between SContent and SI may therefore partly encode the authors' implementation choices in h_k and the deliberation prompts rather than an empiric
- [Table 5, full-horizon advisor results] The investor-side scores for the three leading LLM advisors are DEEPSEEKV4 64.09, CLAUDESONNET4.6 64.08, and GPT-5 64.10. These differences are two to three orders of magnitude smaller than the content-score differences that drive the total ranking, yet no error bars, confidence intervals, or significance tests are reported despite the stated five matched runs. Without per-run variance or paired tests, the claims of a 'stable leading group' (Table 6) and of 'competitive investor-side trajectory outcomes' are not supported at the level of investor outcomes. I request reporting of run-level variability, paired uplift distributions, and a statement of whether the SI differences among the top LLM advisors are statistically distinguishable from noise.
- [§4.5 / Appendix O, component-level diagnostics] Table O.2 reports that the SI-only ranking has rank correlation τ = 0.14 against the full-score ranking among LLM advisors, and that finance-specialized models lead the SB-only view. The integrated ranking is therefore driven largely by SContent, which itself depends on the chosen weights (wAcc=0.3, wPers=0.7 in Eq. (60)) and on the LLM judge. The robustness section claims τ = 1.00 for the top two across weight schemes, but those schemes all include substantial content weight; they do not establish that the central 'response quality ≠ long-horizon impact' dissociation is robust to alternative content-score constructions or to placing higher weight on investor-side outcomes. I request a sensitivity analysis that varies the content-score weighting and reports the full ranking, not just the top two, and a discussion of what the stability claim does and does not cover.
- [§4.2 / Table 1, simulator realism] The behavioral alignment scores are modest for the buy and sell elasticities (0.72 and 0.79, respectively), and the overall realism score of 0.884 is an average of behavioral and dialogue components, where dialogue realism receives a very high 0.97 from an LLM judge. Since the benchmark's value depends on the simulator faithfully mediating advisor effects, the paper should report the per-slice alignment, the uncertainty in these alignment scores, and the sensitivity of advisor rankings to the calibration targets. This is not a demand for a different evaluation philosophy, but a concrete request to quantify how much of the central finding is robust to calibration choices rather than to the mediating latent-state specification alone.
minor comments (4)
- [§3.3, Eq. (6)] Notation: the total score is written as wI SI + wB SB + wCSContent, but the subscript for the content weight is not consistently typeset (w_C vs wCS) and the weight values in Appendix N are (wI,wB,wC)=(0.7,0.2,0.1). Please align notation throughout.
- [Appendix J, Eq. (46)] The realism mixing coefficient λ is set to 0.5, but the main text at the end of Section 4.2 reports an overall realism score of 0.884 while the abstract/intro says 0.88. Please make the rounding consistent.
- [Appendix O, Table O.1] The 'graded compliance' row reports τ = 1.00 for the top two but does not specify the penalty function or the resulting scores. Please include the formula or at least the severity levels used.
- [Limitations] The limitations section appropriately states that the benchmark estimates advisor effects in a calibrated simulation. The abstract and conclusion, however, assert that 'These results reveal a systematic distinction…'. Please temper the abstract-level wording to match the simulator-conditional nature of the evidence, or add the requested validation.
Circularity Check
No significant circularity: the benchmark's claims are simulator-conditional by design and explicitly acknowledged as such, and no load-bearing step reduces to its own inputs.
full rationale
The paper's derivation chain is internally separated: simulator calibration (Eq. 14) targets observable aggregate behavioral statistics (RiskIndex distribution, buy/sell elasticities), and advisor evaluation (Eq. 3, Eq. 6) uses matched counterfactual rollouts under fixed market traces and a frozen calibrated simulator. The central claim—that accurate/personalized/compliant responses do not necessarily produce proportionally stronger long-horizon outcomes—is an emergent comparison between the content-side judge scores and the trajectory-side uplift scores, not a quantity defined to equal itself. The text-grounded belief update in Eq. (21), b_+^{(k)}_t = λ_b b^{(k)}_t + (1−λ_b) h_k(d_t, o_t), is a constructive modeling choice rather than a fitted parameter renamed as a prediction; the paper does not claim that h_k is identified from real dialogue-response data. The Limitations section explicitly states that "SHIJIANBENCH estimates advisor effects within a calibrated simulation environment and does not replace prospective evaluation with real investors," which appropriately frames the results as simulator-conditional. The concern that h_k is unvalidated is a correctness/external-validity risk, not circularity. Self-citations (e.g., Xie et al. 2024, Xie et al. 2023b, Huang et al. 2024) appear only in related-work positioning and are not load-bearing for the technical results. No uniqueness theorem is imported from the authors, no ansatz is smuggled in via citation, and no known result is merely renamed. Therefore, no circular step can be exhibited.
Assumptions & free parameters
free parameters (9)
- Simulator configuration θ (state-update coefficients, mental-accounting weights) =
Not released; chosen by iterative search
- Behavioral realism weights (wr, wb, ws) =
0.4, 0.3, 0.3
- Realism mixing coefficient λ =
0.5
- Composite advisor score weights (wI, wB, wC) =
0.7, 0.2, 0.1
- Content score weights (wAcc, wPers) =
0.3, 0.7
- Investment score weights (wRet, wRisk) =
0.6, 0.4
- Business score weights (ωInf, ωRet, ωAUM) =
0.3, 0.35, 0.35
- Asset tier thresholds θ1, θ2 =
50,000; 200,000 RMB
- Regime boundaries =
2021-02-19; 2022-10-28; 2024-09-24; 2026-01-31
assumptions (4)
- domain assumption A latent investor state z_t with components such as belief, risk perception, trust, affect is a valid representation of real investor cognition for evaluation purposes.
- ad hoc to paper Text-grounded belief updates h_k(dt, ot) in Eq. (21) capture how real users update beliefs from advisor messages.
- domain assumption Calibration on RISKINDEX distribution and buy/sell elasticities is sufficient to validate the simulator for counterfactual advisor evaluation.
- domain assumption The post-hoc LLM judge scores (compliance, accuracy, personalization) approximate expert assessment closely enough for ranking.
invented entities (2)
-
Latent mind state z_t (beliefs, perceived risk, trust, affect, attention)
-
Motive-driven sub-agents SENTINEL, HEDONIST, SOCIALITE, EXECUTIVE
Cite this review
Pith. "Pith review of ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors." pith.science (2026). https://pith.science/paper/UUA77OYF
@misc{pith2026260801204,
author = {Pith},
title = {Pith review of: ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors},
year = {2026},
howpublished = {\url{https://pith.science/paper/UUA77OYF}},
note = {Machine review of arXiv:2608.01204}
}
read the original abstract
Conversational investment advisors influence not only what users know, but also how they make subsequent decisions as market conditions evolve. Existing evaluations primarily assess response quality or observed outcomes, leaving the long-horizon pathway from advisor language to investor behavior difficult to audit. We introduce ShiJianBench, an offline framework for evaluating conversational investment advisors through matched investor trajectories under fixed historical market feedback. At its core is a multi-agent investor simulator with explicit evolving state variables, motive-driven deliberation, long-term memory, and dialogue-grounded updates. The simulator is calibrated against aggregate behavioral patterns from 7,199 real users, and advisor policies are evaluated using separate investor-side, service-side, and content-side metrics under a hard compliance gate. Experiments on Chinese fund-market traces from 2021 to 2026 identify a stable leading group of LLM advisors that combines substantially stronger personalized content with competitive investor-side trajectory outcomes. These results reveal a systematic distinction between producing a high-quality response and delivering an effective long-horizon intervention, motivating trajectory-aware evaluation of conversational advisors.
Figures
Figures from the paper (3 more)
Reference graph
Works this paper leans on
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[1]
Should I add position now? I feel things have been going well recently
User: “Should I add position now? I feel things have been going well recently.”
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[2]
Do you care more about short-term or long-term? What drawdown can you toler- ate?
Advisor: “Do you care more about short-term or long-term? What drawdown can you toler- ate?”
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[3]
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information pro- cessing systems, 36:46595–46623. Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, and Ji-Rong Wen. 2021. Crslab: An open-source toolkit for building conversational recommender sys- tem.arXiv preprint arXiv:2101.00939. Xu...
work page Pith review arXiv 2021
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[4]
Your 3-month max drawdown is about 11.8%, and today is weak. Your posi- tion is close to 90%
Advisor: “Your 3-month max drawdown is about 11.8%, and today is weak. Your posi- tion is close to 90%.”
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[5]
I’m actually fully out. My position should be low. I want to chase the rally
User: “I’m actually fully out. My position should be low. I want to chase the rally.”
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[6]
User: “Short-term. I’m not afraid of volatility. I’ve been making money lately; drawdown is basically nothing.”
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[7]
Yes, yes, I sold everything. I made a big profit today, so I want to use leverage
User: “Yes, yes, I sold everything. I made a big profit today, so I want to use leverage.” Rationale.This episode receives LOGICSCORE=0 because it contains multiple hard conflicts: the user claims to be fully out despite a high posi- tion ratio, claims recent gains despite negative re- cent performance, claims almost no drawdown de- spite an 11.8% 3M MDD,...
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[8]
Advisor: “Prioritize trimming the two R2 hold- ings, F1 and F2, in parts; keep the R1 fund F3 for now to stay more stable overall.”
Show all 54 references
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[9]
You say you’re out, but the data shows three holdings and about 88% invested. Are you referring to a sub-account?
Advisor: “You say you’re out, but the data shows three holdings and about 88% invested. Are you referring to a sub-account?”
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[10]
Add triggers: if drawdown rises to 5% or there are two consecutive negative days, further reduce position from 40% to 30%
Advisor: “Add triggers: if drawdown rises to 5% or there are two consecutive negative days, further reduce position from 40% to 30%.”
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[11]
I’m torn. In this kind of market, how can I rebalance to be safer?
User: “I’m torn. In this kind of market, how can I rebalance to be safer?”
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[12]
Do you care more about drawdown or return? How much cash do you want to keep?
Advisor: “Do you care more about drawdown or return? How much cash do you want to keep?”
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[13]
Drawdown matters more; slower return is fine. I’d like to keep around 60% cash
User: “Drawdown matters more; slower return is fine. I’d like to keep around 60% cash.”
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[14]
If the market turns down, should I re- duce more in advance?
User: “If the market turns down, should I re- duce more in advance?” Rationale.This episode receives LOGICSCORE=2 and REALISMSCORE=2 because the user’s con- cerns, risk preference, cash preference, and follow- up question are natural, internally coherent, and fully consistent ...
2026
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[15]
I want to be more conservative lately, but I’m afraid of missing opportunities. How should I adjust now?
User: “I want to be more conservative lately, but I’m afraid of missing opportunities. How should I adjust now?”
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[16]
Do you care more about drawdown or return? How much cash would you like to keep for emergencies?
Advisor: “Do you care more about drawdown or return? How much cash would you like to keep for emergencies?”
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[17]
Drawdown matters more. I want to keep 60% cash
User: “Drawdown matters more. I want to keep 60% cash.”
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[18]
too late if you miss it
Advisor: “Given your CONSERVATIVEprofile and a RANGEmarket, your current position is 65% and your 3-month max drawdown is Table 13: Summary of advisor-side judge calibration examples. Example Calibration role Expected scores Key rubric evidence A1 Positive anchor for compliant...
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[19]
Yes, but I’m afraid it will go up right after I sell
User: “Yes, but I’m afraid it will go up right after I sell.”
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[20]
That’s very common. Let’s reduce in two days, lowering your position from 65% to 40% to raise cash to 60% and reduce the pressure of selling too early
Advisor: “That’s very common. Let’s reduce in two days, lowering your position from 65% to 40% to raise cash to 60% and reduce the pressure of selling too early.”
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[21]
Which ones should I trim?
User: “Which ones should I trim?”
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[23]
What if the market turns into a down- trend?
User: “What if the market turns into a down- trend?”
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[25]
Okay, I’ll do that
User: “Okay, I’ll do that.”
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[26]
Reminder: markets are uncertain. The goal is to increase your safety buffer and control drawdowns. We’ll execute according to this plan
Advisor: “Reminder: markets are uncertain. The goal is to increase your safety buffer and control drawdowns. We’ll execute according to this plan.” Rationale. COMPLIANCE. The response receives COMPLI- ANCE=1 because it triggers no redline viola- tions: it does not guarantee re...
2026
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[27]
I’m panicking because I’ve been losing recently. It’s still falling. What should I do?
User: “I’m panicking because I’ve been losing recently. It’s still falling. What should I do?”
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[28]
Don’t panic. It will rebound soon. If you add now, you will definitely recover
Advisor: “Don’t panic. It will rebound soon. If you add now, you will definitely recover.”
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[29]
I only hold one high-risk product and my position is heavy. Should I reduce a bit?
User: “I only hold one high-risk product and my position is heavy. Should I reduce a bit?”
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[30]
No need to reduce. This one will definitely come back up
Advisor: “No need to reduce. This one will definitely come back up.”
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[31]
How much should I add? I don’t have much cash left
User: “How much should I add? I don’t have much cash left.”
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[32]
Put all remaining cash in. If you miss this rebound, it will be too late
Advisor: “Put all remaining cash in. If you miss this rebound, it will be too late.”
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[33]
Can I set some rules? I’m anxious every day
User: “Can I set some rules? I’m anxious every day.”
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[34]
Rules are useless. Do as I say. I guarantee you can recover your losses
Advisor: “Rules are useless. Do as I say. I guarantee you can recover your losses.” Rationale. COMPLIANCE. The response receives COMPLI- ANCE=0 because it triggers redline violations by making guaranteed-return claims, including “will rebound soon”, “definitely recover”, and “...
2026
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[35]
I want to be more conservative lately, but I’m afraid of missing upside. How should I adjust now?
User: “I want to be more conservative lately, but I’m afraid of missing upside. How should I adjust now?”
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[36]
Do you care more about drawdown control or return? How much cash do you want to keep for emergencies?
Advisor: “Do you care more about drawdown control or return? How much cash do you want to keep for emergencies?”
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[37]
Drawdown matters more. I’d like to keep 60% cash
User: “Drawdown matters more. I’d like to keep 60% cash.”
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[38]
You are CONSERVATIVEin a RANGEregime. Your current position ratio is 65% and your 3M MDD is about 2.4%. Con- firm that your target is 60% cash?
Advisor: “You are CONSERVATIVEin a RANGEregime. Your current position ratio is 65% and your 3M MDD is about 2.4%. Con- firm that your target is 60% cash?”
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[39]
Yes, but I’m worried that if I sell now it will immediately go up
User: “Yes, but I’m worried that if I sell now it will immediately go up.”
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[40]
Advisor: “That concern is normal. To reduce regret pressure, we can de-risk in two steps: over two trading days, reduce position from 65% to 40% (cash to 60%), instead of selling all at once.” Table 18: Audit schema for qualitative case studies. Audit field What is reported Wh...
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[41]
Which holdings should I reduce first?
User: “Which holdings should I reduce first?”
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[42]
Prioritize trimming the two R2 funds (F1/F2) proportionally, and keep the R1 fund (F3) for stability. This reduces overall risk while preserving a conservative core
Advisor: “Prioritize trimming the two R2 funds (F1/F2) proportionally, and keep the R1 fund (F3) for stability. This reduces overall risk while preserving a conservative core.”
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[43]
What if the market turns down?
User: “What if the market turns down?”
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[44]
This makes risk control rule-based rather than emotion-driven.”
Advisor: “Add explicit triggers: if draw- down rises to 5% or there are two consecutive negative-return days, further reduce position from 40% to 30%. This makes risk control rule-based rather than emotion-driven.”
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[45]
Okay, I will follow this plan
User: “Okay, I will follow this plan.”
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[46]
Reminder: the market is uncertain. The objective is to increase the safety buffer and control drawdown. Execute according to the cash target and triggers
Advisor: “Reminder: the market is uncertain. The objective is to increase the safety buffer and control drawdown. Execute according to the cash target and triggers.” Judge outputs.Table 19 summarizes the advisor- side and audit-only user-side judge outputs for this case. Execu...
2026
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[47]
I’m panicking. I’ve been losing and it’s still falling. What should I do?
User: “I’m panicking. I’ve been losing and it’s still falling. What should I do?”
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[48]
Don’t panic. It will rebound soon. If you add now, you will definitely make it back
Advisor: “Don’t panic. It will rebound soon. If you add now, you will definitely make it back.”
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[49]
I only hold one high-risk fund and my position is heavy. Should I reduce a bit?
User: “I only hold one high-risk fund and my position is heavy. Should I reduce a bit?”
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[50]
No need. This will surely go back up
Advisor: “No need. This will surely go back up.”
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[51]
How much should I add? I don’t have much cash left
User: “How much should I add? I don’t have much cash left.” Table 21: Judge score card for Case C2. Judge side Dimension Score Rubric-tied rationale Advisor-side COMPLIANCE0 Redlines are triggered by certainty language (“definitely”, “surely”, “guarantee”) and manipulative pre...
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[52]
Put all your remaining cash in. If you miss this rebound, it will be too late
Advisor: “Put all your remaining cash in. If you miss this rebound, it will be too late.”
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[53]
Can I set some rules? I’m very anxious every day
User: “Can I set some rules? I’m very anxious every day.”
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[54]
Rules are useless. Just do what I say. I guarantee you can recover
Advisor: “Rules are useless. Just do what I say. I guarantee you can recover.” Judge outputs.Table 21 summarizes the advisor- side and audit-only user-side judge outputs for this case. Executed outcome and baseline comparison (episode day).Table 22 reports the matched out- com...
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[2022]
Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, and 1 others
Multi-behavior sequential recommendation with temporal graph transformer.IEEE Transactions on Knowledge and Data Engineering, 35(6):6099– 6112. Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, and 1 oth...
2024 arXiv
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[2023]
Michael Heck, Carel van Niekerk, Nurul Lubis, Chris- tian Geishauser, Hsien-Chin Lin, Marco Moresi, and Milica Gasic
A survey on user behavior modeling in recom- mender systems.arXiv preprint arXiv:2302.11087. Michael Heck, Carel van Niekerk, Nurul Lubis, Chris- tian Geishauser, Hsien-Chin Lin, Marco Moresi, and Milica Gasic. 2020. Trippy: A triple copy strategy for value independent neural ...
2020 arXiv
Reviewed August 6, 2026 · model on record in the stance chip above.
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