zfnwong / aldi

This is our implementation for ALDI.

ALDI

ALDI is a state-of-the-art framework for cold-start recommendation. It addresses the three difference (i.e., rating distribution difference, ranking difference and identification difference) between the warm model and cold model in a general knowledge distillation-based framework.

Quick Start

• Run python main.py --embed_meth bprmf --dataset CiteULike --model ALDI.

Run From Scratch

1. Pre-process the dataset

   • Go the data directory by cd data/.
   • Split the dataset by python split.py --dataset CiteULike.
   • Formulate the data and by python convert.py --dataset CiteULike. The processed results will be stored in $root_path/data/$dataset_name/

2. Pre-train warm model.

   • Go back to the root directory.
   • Go to the the directory of warm model by cd warm_model/.
   • Pre-train the warm model Matrix Factorization by running python bprmf.py --dataset CiteULike. The trained embeddings will be also stored in $root_path/data/$dataset_name/

3. Train and evaluate cold model.

   • Go back to the root directory.
   • Train and evaluate ALDI by running python main.py --embed_meth bprmf --dataset CiteULike --model ALDI.

aldi's Issues

another dataset

can you provide another dataset, please.