Research on User Profile and User Behavior of Integrating Big Data Platforms
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
This paper discusses the construction and analysis method of user behavioral portrait by the data provided by the electric power platform in the big data environment. Firstly, it introduces the construction and analysis of user profiles based on big data platforms, which covers the construction of user basic attribute profiles, user behavioral characteristics profiles, user product characteristics profiles and user interaction characteristics profiles from different dimensions. Secondly, for the electric power sector, the article discusses the analysis of big data provided by electric power platforms to better understand user behavior and trends in energy consumption. The article proposes a method for constructing a behavioral portrait of power users based on big data analysis, including the construction and management of a user label library and the process of constructing a behavioral portrait of power users based on the improved K-mean algorithm. Finally, the effectiveness and accuracy of the method of this paper are verified by experimental analysis. Overall, this paper provides some guidance and reference for the analysis of user behavior in the field of electric power by exploring the method of user behavior portrait construction with the data provided by the electric power platform in the big data environment.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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