Design of Computational Identification Model and Optimization Strategy for Consumer Demand Based on Multimodal Data Fusion
Bibliographic record
Abstract
This paper draws a framework for constructing user demand modal information, uses crawler technology to obtain online review text information, processes the text information, and mines relevant consumer demand information.The LDA topic model is used to extract the topics of consumer concern from the online comments, identify the topics of consumer demand and clarify the concern degree of each demand.The KANO model is proposed to establish a consumer demand classification method based on the KANO model by combining product characteristic attributes and consumer demand information.Examine the theme discrimination performance of the LDA model on the hotel category, footwear category, and food category datasets.Combine the preprocessed user demand data to statistically quantify user demand for quantitative Kano transformation.Classify user demands into Kano categories and calculate the priority order of user demands to get the product optimization strategy.The weighted order of consumers' demands for automobiles is footrest, cigarette lighter, antenna, window, low beam, key, etc. in order.It can be found that automobile consumers pay more attention to the needs of antenna, cigarette lighter, pedals, and enhancement of accessory functions.As a result, automobile manufacturers should increase the seat comfort, improve power, enhance the flexibility of shifting such aspects of the whole vehicle handling experience, in addition to improving the lights, keys and other car quality related needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".