Research on e-Commerce User Behavior Analysis Based on Big Data Collaborative Recommendation Algorithm
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
With the rapid development of Internet, users tend to purchase their favorite products through Internet transactions and online payment. The general trend of e-commerce development in China is that physical trading places are gradually replaced by online trading platforms on the Internet. In this paper, the restricted Boltzmann machine based on category conditions is used to describe the user's own interest preference by using the objective label of the project itself. In this process, only the project information that the user has scored is used, which strengthens the user's personalized needs. The method fully mines user behavior information, replaces commodity content big data with user behavior information as a recommended data set, and can actively push commodity content that users may be interested in to users. Experimental results show that the accuracy of RBM ( Restricted Boltzmann machine) model with nearest neighbor is higher than that of the original model, and the anti-over-fitting ability of the model is also improved.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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