Bibliographic record
Abstract
Various Web systems and services currently provide a great deal of benefits to users, with Web interaction becoming increasingly important in research and business. Such Web interaction has been realized through related technologies as interaction design, interactive information retrieval, interactive intelligent systems, personalization, user interfaces and interactive machine learning. However, each study and development in such different fields has been done independently, which might discourage us from studying Web interaction from an unified view of human-system interaction and making Web interaction more intelligent by applying AI and computational intelligence. Guest Editors (Seiji Yamada, Tsuyoshi Murata, and Yasufumi Takama) organized an Intelligent Web Interaction Workshop 2009 (IWIf09) in Milano, Italy, last year to bring together researchers in diversified fields including Web systems, AI, computational intelligence, humancomputer interaction and user interfaces. Held jointly with 2009 IEEE/WIC/ACM International Conference on Web Intelligence (WI-2009), IWIf09 produced 14 outstanding papers - an acceptance rate of 50%, and active discussions among speakers and participants. A subsequent workshop Intelligent Web Interaction Workshop 2010 (IWIf10) will be held in Toronto, Canada in this September. This special issue presents intelligent Web interaction as a new and promising research field. Speakers selected from among those at IWIf09 were encouraged to submit papers for this issue. The submissions were then reviewed for relevance, originality, significance and presentation based on JACIII review criteria. This special issue consists of five papers which describe excellent studies on Web interface, Web systems, Web credibility, constrained clustering for interactive Web application and graph analysis on the Web. The acceptance rate was 56%. All papers introduce promising approaches and interesting results that readers will find inspiring. We strongly believe intelligent Web interaction has tremendous potential as a new, active field of research, and we hope this issue will motivate researchers to expand studies on intelligent Web interaction.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.242 |
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".