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

pip install -r requirements.txt

Python 3.9+.

Quick start

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:

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

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

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:

{
  "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	`
Recall	`
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

@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.