Improving web site search using web server logs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the success of global search engines, web site search engines are still suffering from poor performance. Since a web site is different from the whole web in link structure, access pattern, and data scale, it is not always successful when the methods which improve the performance of web search are applied to web site search. In this paper, we propose a novel algorithm to improve the retrieval performance by using web server logs. Web server logs are grouped into different sessions and the relationships of web pages in the session are analyzed based on their similarities. Then, a new web page representation is generated. Anchor text is used to create another representation. They are combined with original text-based representation in web site search. Two kinds of combination methods are investigated and tested: combination of document representations and combination of ranking scores. Our experimental results show that our algorithm can improve the retrieval accuracy for the four retrieval models we tested: Inference Network Model, Okapi Model, Cosine Similarity Model and TFIDF Model. The highest performance increase from web log analysis is from TFIDF model, and overall, inference network model with web log information achieves the best result.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it