Improving road trip attraction recommendations by resolving conflicting preferences: a knowledge-enhanced non-compensatory group decision approach
Bibliographic record
Abstract
Purpose In recent years, road trips have become a popular travel mode in China. However, existing attraction recommendation methods often overlook conflicting preferences among tourists and key factors specific to road trips, resulting in suboptimal recommendations. To address this issue, we propose a knowledge-enhanced non-compensatory group decision-making approach to improve the accuracy of road trip attraction recommendations. Design/methodology/approach First, our approach constructs a comprehensive tourism knowledge graph by integrating information about both tourists and attractions. Second, an 11-dimensional modeling framework is proposed to better portray tourist preferences and attraction characteristics in the road trip context. Finally, the non-compensatory group decision-making algorithm, Elimination Et Choix Traduisant la REalité III (ELECTRE-III), is applied to model each tourist's preferences within the group and rank attractions for the tourist group. Findings Experimental results demonstrate the effectiveness of the proposed method in road trip attraction recommendation. Compared to existing approaches, our method maintains a recommendation failure rate below 15% across varying levels of conflict rates and group sizes, consistently outperforming all baseline methods. Statistical analysis further confirms that the non-compensatory mechanism effectively identifies attractions that align with the collective preferences of tourist groups. Originality/value This paper proposes a novel method for recommending attractions to road trip tourist groups by integrating non-compensatory group decision-making with knowledge integration. The approach effectively incorporates individual preferences while maintaining a low recommendation failure rate, thereby enhancing both recommendation performance and overall tourist satisfaction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".