A Web Intelligence Solution to Support Recommendations from the Web
Bibliographic record
Abstract
Big data are everywhere. World Wide Web and social networks are examples of these big data. They have become a vast data production and consumption platform, at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations, under divergent distributed settings. Embedded in these big web data is implicit, previously unknown and potentially useful information and knowledge that awaited to be discovered. This calls for web intelligence solutions, which make good use of data science, data mining (especially, web mining) and social network analysis to discover useful knowledge and important information from the web (e.g., web of people/things). Such a web often consists of vertices (i.e., people/things) and edges (i.e., connections among people/things). When modeling a social network as a web of people, these edges can be undirected (e.g., for mutual friendships) or directed (e.g., for capturing a social entity who is following another social entity). In this paper, we present a web intelligence solution to discover interesting knowledge (e.g., most-followed people or most-referenced web pages) from these social connections. Due to the dynamic nature of the web, vertices and/or edges may be changed (e.g., added or deleted) over time. Hence, our solution is designed in such a way that it discovers knowledge not only from a static web but also from a dynamic web. Evaluation on real-life web data demonstrates the effectiveness and practicality of our solution for discovering knowledge and supporting recommendations from the web.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".