在社交媒体蓬勃发展的背景下，精准的用户兴趣画像成为个性化推荐、内容运营与用户行为分析的核心支撑。本文提出了一种融合用户静态信息、动态博文内容与时序兴趣特征的用户兴趣画像构建及预测方法。首先，整合用户基本信息生成词云图，实现用户兴趣的可视化表征；其次，以 10 条博文为时间窗口，借助大语言模型（LLM）为每个窗口生成精细化兴趣标签，捕捉用户兴�
active 2025-12-26 → 2026-01-08 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 14 days from 2025-12-26 to 2026-01-08. Pushes: 0 total, peak 0 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
Nobody pushed, opened or commented here in the loaded window — this repo's activity is stars and forks only.
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.