PCFinder: an intelligent product recommendation agent for e-commerce
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
There are many e-commerce applications on the Web. A common shortcoming is the lack of customer service and marketing analysis tools in most e-commerce web sites. In order to overcome this problem, we have constructed an intelligent agent based on Case-Based Reasoning (CBR) and collaborative filtering, which we have included in our product recommendation system, called PCFinder. This system was four main characteristics. The first is applying novel methodologies based on CBR to an e-commerce application. We propose a heuristic to represent an Order-Based Similarity Measure, together with the method of weight modification and adaptation. The second is applying CBR and collaborative filtering techniques to make our intelligent agent more efficient and effective. We also apply clustering analysis techniques to assist our intelligent agent for grouping the customers according to their long-term profiles in order to analyze the user profiles (external attributes) and provide some suggestions of the items (internal attributes) of the product. The third is introducing a method for constructing product recommendation systems: from architecture to methodologies and from applied technologies to implementations. The last is providing a graphic-building wizard based on clustering analysis of the past purchasing history to the management staff for analyzing the marketing tendencies.
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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 | 0.000 |
| 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 it