# MVP Configuration - Preference-Weighted Scoring (2025-style adaptive fusion)
# Third comparison method: fuse similarity with feature-level preference weight
# Reference: Personalized ROI recommendation through adaptive fusion of multi-dimensional user preferences (2025)
model:
  model_name_or_path: "/home/ubuntu/codebase/yexijia/保研/LlamaFactory/models/qwen2.5-7b"
  adapter_name_or_path: null
  template: "qwen"
  finetuning_type: "lora"
  trust_remote_code: true

inference:
  device: "cuda"
  temperature: 0.7
  max_new_tokens: 512
  top_p: 0.9
  do_sample: true
  use_fp16: true

mining:
  default_min_participation: 0.6
  default_max_pattern_size: 5
  default_priority: "confidence"

data:
  data_path: "./data/beijing_poi.json"

logging:
  level: "INFO"
  log_file: "./logs/mvp.log"

stage0:
  enabled: true
  decay_lambda: 0.05
  min_confidence: 0.6

# Stage3: Preference-Weighted (no contrastive, with adaptive fusion)
stage3:
  enabled: true
  use_contrastive: false
  use_preference_weighted: true   # 2025-style: score *= preference_weight(pattern, liked, disliked)
  retrain_interval: 20
  margin: 0.3
  learning_rate: 0.001
  hidden_dim: 256
  epochs: 10
  batch_size: 32
  alpha: 0.7
  use_cosine_loss: true
  model_save_path: "./models/preference_encoder_pw.pth"
  user_id: "user_001"

stage4:
  enabled: true
  default_rounds: 3
  max_rounds: 5
  fusion_alpha: 0.6
