Adaptive Web Crawling Strategies Based on Ontological User Interest Modeling for Personalized Content Retrieval
Bibliographic record
Abstract
The exponential growth of web content necessitates efficient and personalized methods for information retrieval. This research introduces an innovative approach to web crawling, centered on adaptive strategies guided by ontological user interest modeling, aiming to enhance the precision of personalized content retrieval. The proposed method employs a dynamic user interest model, constructed, and continually refined through user interactions, leveraging ontology to represent and understand the complex and evolving nature of user interests. In contrast to traditional web crawlers that operate on fixed algorithms, the adaptive crawler anticipates changes in user interests and adjusts its strategies, accordingly, ensuring relevance and timeliness of the retrieved content. The system architecture integrates a multi-faceted profiling mechanism that captures and evolves with user preferences, combined with an intelligent crawling algorithm that prioritizes content based on predicted user interest levels. Empirical results demonstrate a significant improvement in retrieval precision and efficiency, highlighting the crawler's ability to discern and adapt to the nuanced shifts in user interests. Furthermore, the research delves into the implications of such personalized crawling on user privacy and web dynamics, offering insights and recommendations for a balanced approach. This work contributes to the fields of semantic web, personalized content retrieval, and adaptive systems, proposing a novel pathway for the next generation of intelligent web crawlers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".