Using Distinctive Information Channels for a Mission-based Web Recommender System
Bibliographic record
Abstract
Web recommender systems anticipate the information needs of on-line users and provide them with recommendations to facilitate and personalize their navigation. There are many approaches to build such systems. Among them, using web access logs to generate users' navigational models to build a web recommender system is a popular approach, given its non-intrusiveness. However, using only one information channel, namely the web access history, is often insu#cient for accurate recommendation prediction. We advocate the use of additional information channels available to better model user navigational behavior. In this paper, we investigate a novel hybrid web recommender system, which combines the access history, the content of visited pages, as well as the connectivity between web resources in a web site to model users' concurrent information needs and then generate users' navigational patterns. Our experiments show that the combination of the three channels in our system significantly improves the quality of the web site recommendation, and each additional channel used contributes to this improvement. In addition, we discuss cases on how to reach a compromise when not all channels are available.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".