Behavior2Value: Benchmarking and Empowering LLMs for Consumer Value Measurement from E-commerce Behaviors
Peixuan Hou, Bin Chen, Li He, Jian Xu, Bo Zheng, Xiuli Ma, Guojie Song
cs.CL · cs.LG
Quick verdict
New benchmark for consumer-value inference from e-commerce behavior; fine-tuned verification model beats prompted LLMs, but lacks significance tests and public data.
Summary
The paper introduces Behavior-to-Value (B2V): inferring consumer value orientations (e.g., price sensitivity, brand trust, perceived risk) from e-commerce behavior trajectories. The authors build the E-commerce Consumption Value Taxonomy (ECVT) with 30 fine-grained constructs organized under the five Theory-of-Consumption-Values dimensions, induced from Amazon Reviews 2023 and refined by psychology experts against established scales. On this basis they present B2V-Bench: 5,440 real purchase-decision episodes from anonymized Taobao logs (13 monthly snapshots, 2025-03 to 2026-03), each averaging 341 pre-purchase actions across 25 behavior types, labeled with 28 constructs plus a none option. Labels are produced by AutoPVQ, a PVQ-style multi-agent pipeline over GPT-5.4 behavioral summaries, with human review; the 544-episode test set is annotated from scratch by three psychology experts. Their model, B2V-Verifier, fine-tunes Qwen3-8B with Value Verification Tuning: rather than predicting labels directly, it verifies for each candidate value whether trajectory evidence is supported, unsupported, or insufficient. It reaches Macro-F1 0.615 versus 0.417 for the best prompted baseline (GPT-5), with consistent NDCG and primary-label gains; ablations support each design choice. Main limitations: no significance tests or seed variance; the headline '34%' gain is not derivable from Table 3; training labels inherit LLM-assisted annotation; data is Taobao-only and not yet released.
Review complete
Assessment at a glance
6
risk flags identified
● high 0
● medium 3
● low 3
4 strengths · 5 weaknesses · 5 author questions
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- B2V is a new task, and ECVT (30 fine-grained constructs across the five TCV dimensions) is the first e-commerce-specific consumption value taxonomy.
- B2V-Bench is the first B2V dataset and benchmark: 5,440 anonymized Taobao purchase-decision episodes averaging 341 pre-purchase actions across 25 behavior types, labeled with 28 ECVT constructs plus a none option.
- B2V-Verifier (Qwen3-8B fine-tuned with Value Verification Tuning) outperforms ten strong LLM baselines on all reported metrics, e.g., Macro-F1 0.615 versus 0.417 for the best prompted baseline.
- Explicitly modeling evidence sufficiency (supported/unsupported/insufficient per candidate value) is the key mechanism reducing over-attribution, since removing the verification formulation drops Macro-F1 from 0.615 to 0.548.
Strengths
Genuinely new task with a far richer annotation unit than prior datasets
B2V-Bench uses 5,440 real Taobao purchase-decision episodes averaging 341 actions across 25 behavior types (Table 1), versus 1-step items or 3-6-step sessions in Amazon-M2, MerRec, and YOOCHOOSE, capturing pre-purchase deliberation instead of isolated interactions.
Evaluation anchored to independent human expertise
The 544-episode test set is sampled from a pool of 1,000 episodes annotated from scratch by three psychology experts (Section 2.3), decoupling test labels from the AutoPVQ pipeline; backbone selection is validated against experts with Cohen's kappa 0.781 and 86.7% primary agreement (Table 2).
Principled verification formulation with direct ablation support
Comprehensive, multi-perspective baseline sweep and temporal robustness checks
Weaknesses
No inferential statistics anywhere
Headline gains are not derivable from the results table
Training-label provenance risks bias and inflation
Single platform and a one-day observation window
Reproduction currently blocked
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Frequently asked questions
What is the Behavior2Value (B2V) task?
B2V means inferring which consumer values (such as price sensitivity, brand trust, perceived risk, or product safety focus) drove a shopper's purchase decision, from the recorded sequence of pre-purchase actions like searching, comparing products, reading reviews, and adding items to the cart. It goes beyond short-term interest prediction by capturing stable motivations behind purchases.
What is in the B2V-Bench dataset?
B2V-Bench contains 5,440 real purchase-decision episodes built from anonymized Taobao logs covering March 2025 to March 2026. Each episode averages 341 pre-purchase actions across 25 behavior types (search, clicks, review browsing, add-to-cart, etc.) and is labeled with consumer-value constructs from a 30-item taxonomy (28 used in the benchmark, plus a 'none' option); 544 test episodes were annotated from scratch by three psychology experts.
How much better is B2V-Verifier than prompting GPT-5 directly?
On the benchmark's test set, the fine-tuned 8B model reaches Macro-F1 0.615 versus 0.417 for the best GPT-5 prompt configuration (about 47% higher relative F1), and NDCG@1 of 0.824 versus 0.781 for the best prompted baseline overall. The paper's own headline is a 34% multi-label improvement, though its exact reference point is not stated in the main text.
Can I download the B2V-Bench dataset or the code?
Not yet. The paper states the dataset and code will be publicly released upon acceptance, and the underlying Taobao behavior logs are proprietary, so the benchmark currently cannot be reproduced or extended by external researchers.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Appendices B, D, F, G, H, and I are referenced but absent from the extracted text; sequential-baseline numbers, psychometric validation of ECVT, VCOP details, prompt variants, and training hyperparameters could not be verified.
- Figures 1-5 are available only as captions and embedded descriptions; the label co-occurrence matrix values and distribution details in Figure 4 are only partially legible from text.
- Table 3 is large (10 models x 4 prompt settings x 11 metrics); headline numbers were cross-checked, but individual baseline cells may contain extraction-level errors.
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