CoCHPL

🚀 The implementation of Chain-of-Choice Hierarchical Policy Learning for Conversational Recommendation (DASFAA 2024).

1. Training

python train.py --data_name <data_name>

1.1 Optional arguments

For more detailed argument configuration, please refer to RL/rl_option_critic.py

parser.add_argument('--data_name', type=str, default=LAST_FM_STAR, choices=[LAST_FM_STAR, YELP_STAR, BOOK, MOVIE])
parser.add_argument('--max_rec_step', type=int, default=10, help='max recommend step in one turn')
parser.add_argument('--max_ask_step', type=int, default=3, help='max ask step in one turn')
parser.add_argument('--cand_feature_num', type=int, default=10, help='candidate sampling number')
parser.add_argument('--cand_item_num', type=int, default=10, help='candidate item sampling number')
parser.add_argument('--max_turn', type=int, default=15, help='max conversation turn')

2. Evaluation

python evaluate.py --data_name <data_name> --load_rl_epoch <checkpoint_epoch> --eval_user_size 0

2.1 Optional arguments

For more detailed argument configuration, please refer to RL/rl_option_critic.py

parser.add_argument('--data_name', type=str, default=LAST_FM_STAR, choices=[LAST_FM_STAR, YELP_STAR, BOOK, MOVIE])
parser.add_argument('--eval_user_size', '-eval_user_size', type=int, default=100, help='Select 0 to evaluate the full test dataset')
parser.add_argument('--load_rl_epoch', type=int, default=0, help='the epoch of loading rl model')