| # StreamProfileBench |
|
|
| Streaming user-interest profiling benchmark for Chinese social media platforms. |
| Each task asks an LLM to (a) maintain a rolling persona summary from a stream |
| of a user's posts, and (b) predict which tags from a curated candidate pool the |
| user will engage with in the next time window. |
|
|
| The benchmark evaluates two coupled abilities: |
|
|
| - **Plasticity** — picking up *newly emerging* interests (`Recall_Novelty`). |
| - **Stability** — retaining *persisting* interests (`Recall_Stability`). |
|
|
| It also probes robustness against four classes of distractors (peer-cluster, |
| viral, decayed, random), and forward transfer from accumulated personas. |
|
|
| ## Repository layout |
|
|
| ``` |
| streamprofile_bench_release/ |
| ├── bench_inference.py # main runner: inference + scoring (and --reeval) |
| ├── bench_task.py # prompt formatter (PLATFORM_CONTEXT + format_prompt) |
| ├── stats.py # aggregate eval reports across models into Excel |
| ├── sample_subset.py # sample N% of users per platform for cheap dry-runs |
| ├── data/ |
| │ ├── weibo_inference_tasks.jsonl |
| │ ├── xiaohongshu_inference_tasks.jsonl |
| │ ├── toutiao_inference_tasks.jsonl |
| │ ├── zhihu_inference_tasks.jsonl |
| │ └── douban_inference_tasks.jsonl |
| ├── requirements.txt |
| ├── LICENSE # Apache-2.0 |
| └── README.md |
| ``` |
|
|
| ## Install |
|
|
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| Python 3.9+. |
|
|
| ## Quick start |
|
|
| ```bash |
| export LLM_API_KEY="sk-..." |
| export LLM_API_BASE="https://api.openai.com/v1" # any OpenAI-compatible endpoint |
| |
| # Single platform |
| python bench_inference.py --model gpt-4o-mini --platform weibo |
| |
| # All five platforms |
| python bench_inference.py --model gpt-4o-mini |
| ``` |
|
|
| Outputs go to `results/<model>/`: |
| - `inference_results_<platform>.jsonl` — per-user, per-step predictions, ground truth, persona summaries, and metrics. |
| - `eval_report_<platform>.txt` — text report (`M_bar`, `F1^NS`, `FWT`, cold-start vs persona-augmented, learning curve). |
|
|
| ### Re-evaluate without re-running the model |
|
|
| If you change metric definitions or want to recompute scores from saved |
| predictions, no API calls needed: |
|
|
| ```bash |
| python bench_inference.py --model gpt-4o-mini --reeval |
| python bench_inference.py --model gpt-4o-mini --platform weibo --reeval |
| ``` |
|
|
| This rewrites the `metrics` field in each `inference_results_<platform>.jsonl` |
| and produces a fresh `eval_report_<platform>.txt`. |
|
|
| ### Cheap dry-run on a 20% subset |
|
|
| ```bash |
| python sample_subset.py --ratio 0.2 |
| # produces data/<platform>_inference_tasks_sub20.jsonl |
| ``` |
|
|
| To use the subset, point `bench_inference.py` at the sub-files (e.g. by |
| renaming, symlinking, or temporarily editing `DATA_DIR`). |
|
|
| ### Aggregate results across models into Excel tables |
|
|
| ```bash |
| python stats.py |
| # writes metric_excels/{Precision,Recall,F1^NS,...}.xlsx |
| ``` |
|
|
| Each Excel file has one row per model, one column per platform (plus an |
| `Avg.` column). |
|
|
| ## Configuration |
|
|
| | Variable | Purpose | Default | |
| |------------------------|---------------------------------------------------------------|---------| |
| | `LLM_API_KEY` | API key for your LLM provider | (required) | |
| | `LLM_API_BASE` | OpenAI-compatible base URL | (required) | |
| | `LLM_INSECURE_TLS` | Set `1` to skip TLS verification (e.g. for self-signed dev) | `0` | |
| | `BENCH_MAX_WORKERS` | Parallel inference threads | `16` | |
| | `--model` | Model name passed to the API | (required) | |
| | `--api_url`/`--api_key`| Override the env vars | unset | |
| | `--platform` | One of `weibo / xiaohongshu / toutiao / zhihu / douban` | all | |
| | `--reeval` | Skip inference; re-score saved predictions | off | |
|
|
| ## Data format |
|
|
| One JSON object per line in each `data/<platform>_inference_tasks.jsonl`: |
|
|
| ```json |
| { |
| "user_id": "we_16d82b9a92", |
| "platform": "weibo", |
| "username": "U_010ada10", |
| "bio": "...", |
| "total_steps": 4, |
| "prediction_tasks": [ |
| { |
| "step_id": 1, |
| "total_steps": 4, |
| "date_input": "2025-06-12", |
| "date_target": "2025-06-17", |
| "posts_text": "[1] Content: ...\nTags: ...\n[2] ...", |
| "candidate_pool": ["tag1", "tag2", ...], |
| "ground_truth": { |
| "all_tags": ["..."], |
| "new_tags": ["..."], |
| "keep_tags": ["..."] |
| }, |
| "meta": { |
| "T_keep": ["..."], |
| "T_new": ["..."], |
| "D_decay": ["..."], |
| "D_cluster":["..."], |
| "D_viral": ["..."], |
| "D_random": ["..."] |
| } |
| } |
| ] |
| } |
| ``` |
|
|
| Per platform / step the candidate pool is pre-built as |
| `pool_size = clip(|GT|·4, [10, 50])` with the remaining slots filled with |
| distractors of four types: |
|
|
| - **D_decay** — tags the user engaged with in the *current* batch but does **not** carry forward (interests on the way out). |
| - **D_cluster** — tags from the same semantic cluster as the GT (peer-cluster distractors). |
| - **D_viral** — tags trending platform-wide on the target date (popular but irrelevant). |
| - **D_random** — tags sampled uniformly from the platform's tag vocabulary. |
|
|
| Ground truth is `T_new ∪ T_keep`, where `T_keep = current ∩ future` and |
| `T_new = future \ current`. |
|
|
| ## Metrics |
|
|
| | Metric | Definition | |
| |---------------------|----------------------------------------------------------------------------| |
| | **Precision** | `|pred ∩ GT| / |pred|` | |
| | **Recall** | `|pred ∩ GT| / |GT|` | |
| | **Recall_Novelty** | Recall restricted to `T_new` (plasticity) | |
| | **Recall_Stability**| Recall restricted to `T_keep` (stability) | |
| | **F1^NS** | Harmonic mean of `Recall_Novelty` and `Recall_Stability`, per user, then macro-averaged | |
| | **Error_Peer** | False-positive rate on `D_cluster` (lower is better) | |
| | **Error_Viral** | False-positive rate on `D_viral` | |
| | **Error_Decay** | False-positive rate on `D_decay` | |
| | **Error_Random** | False-positive rate on `D_random` | |
| | **FWT** | Forward transfer: `mean(M_{t≥2}) − M_{t=1}`, per metric | |
|
|
| Aggregation in the report: |
|
|
| - **`M_bar`** — within-user mean across steps, then macro mean across users. |
| - **Cold-start vs persona-augmented** — same aggregation on `step_id == 1` |
| vs `step_id ≥ 2`, plus the delta. Quantifies the value of carrying personas. |
| - **Learning curve** — per `step_id` position, mean across users. |
| |
| ## Anonymization notes |
| |
| This release uses anonymized identifiers: |
| |
| - `user_id` is a hash prefixed with the platform code (`we_` weibo, `xi_` xiaohongshu, `to_` toutiao, `zh_` zhihu, `do_` douban). |
| - `username` values are replaced with placeholders of the form `U_xxxxxxx`. |
| - Free-text fields (`bio`, `posts_text`) retain user-generated content as |
| collected; we do not redact post bodies. |
| |
| If you find any residual PII please open an issue. |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{streamprofilebench2026, |
| title = {StreamProfileBench: Streaming User-Interest Profiling on Chinese Social Media}, |
| author = {...}, |
| year = {2026}, |
| howpublished = {GitHub}, |
| url = {...} |
| } |
| ``` |
| |
| ## License |
| |
| Apache License 2.0. See [LICENSE](LICENSE). |
| |
