Toward Preference and Context-Aware Hybrid Tourist Recommender System Based on Machine Learning Techniques
Bibliographic record
Abstract
With the development of machine learning, to improve the accuracy in recommendation systems, the main purpose of the suggested approach consists of using techniques and algorithms which can predict and suggest relevant tourist services (k-items) to users according to their interests, needs, or tastes. In this research, we describe how machine learning techniques can automatically provide personalized recommendations to the requestors by considering both their preferences and their implicit/explicit contextual information and the current contextual constraints of the points of interest. We build an efficient, intelligent H-RN algorithm that hybridizes both the most known machine learning algorithms, namely Random Forest and Naïve Bayes, with both collaborative filtering techniques (model-based and memory-based technique). Different experiments of our approach as part of a recommender system in the touristic field are performed over the four large real-world datasets. Recommender systems can use H-RN to improve recommendation prediction and reduce the search space of tourist services. Moreover, the results of recall, precision, accuracy, F-measure, and average rate, as well as a set of statistical tests (One-way ANOVA, Diversity) and error metrics (RMSE, MAE) have been discussed to show the improvement of the prediction accuracy of our algorithm compared to the baseline approaches in various settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".