PHDMF

Collaborative Filtering(CF), a well-known approach in producing recommender systems, has achieved wide use and excellent performance not only in research but also in industry. However, problems related to cold start and data sparsity have caused CF to attract an increasing amount of attention in efforts to solve these problems. Traditional approaches adopt side information to extract effective latent factors but still have some room for growth. Due to the strong characteristic of feature extraction in deep learning, many researchers have employed it with CF to extract effective representations and to enhance its performance in rating prediction. Based on this previous work, we propose a probabilistic model that combines a stacked denoising autoencoder and a convolutional neural network together with auxiliary side information (i.e, both from users and items) to extract users and items' latent factors, respectively. Extensive experiments for four datasets demonstrate that our proposed model outperforms other traditional approaches and deep learning models making it state of the art.

Updates for the data format

• Many people ask about the data format about the model. Our model has two kinds of data, one is user side information and the other is item side information.
• For user side information, the dataformat is user_id::binary_value, eg. 456::0010010000100000
• For item side information, the dataformat is user_id::item_id::rating, eg. 456::1::3
• Just see run_test_preprocess.sh for the process of data. Because the original data is too large, I cannot give a dowonload link.

Updates for the use of the model

• 1. run run_test_preprocess.sh for process the data
• 2. run run_test_PHDMF.sh for training the data
• 3. run python rmse.py for test the performance of the model

Note

If you find this model is useful for your research, please cite this paper:

• PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems ACML 2017

1 PHD Model

2 CNN Model

3 aSDAE Model

This is a variant of ConvMF and aSDAE. Certainly, it is based on ConvMF and aSDAE.

We use aSDAE and CNN to generate the user latent factor and item latent factor, respectively.

If you want to use it, pleae install keras and tensorflow ,respectively.

Note that this model can deal with three conditions:

• only user side information (aSDAE model)
• only item side information (ConvMF model)
• user and item side information (PHD model)

Tips：Please make sure you have a good deep learning environment to run these codes.