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KNI

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Knowledge-enhanced Neighborhood Interaction model for recommendation

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Tensorflow implementation of the paper ``An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation``.
This paper is accepted by, and and wins the ``best paper award`` of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data, KDD'19 (DLP-KDD'19), Anchorage, AK, USA.

See our [paper](https://arxiv.org/abs/1908.04032), [poster](./material/kni_poster.pdf), [ppt](./material/kni_presentation.pdf).
If you have any questions, please contact ``kevinqu16@gmail.com`` directly or open a github issue.
I will reply ASAP.

### What's KNI model?

In recommender systems, graph-based models build interaction graphs from historical feedbacks (e.g., user ratings) and side information (e.g., film tags, artists), 
and utilize the rich structural information to boost recommendation performance.

![graph-methods](https://raw.githubusercontent.com/Atomu2014/KNI/master/material/graph-methods.png)

Due to the complex structures and large scales (of the graphs), it is hard to make predictions directly, 
thus the existing approaches turn to encoding the meticulous structures into user/item embeddings.
Since the rich structural information is compressed in only 2 nodes and 1 edge, 
we concern the valuable local structures are not fully utilized in previous literature,
which we call the ``early summarization issue``.

After reviewing the existing methods, we derive a general architecture of these methods, 
and propose ``Neighborhood Interaction`` (NI) model to make predictions from the graph structures directly.
NI is further integrated with graph neural networks (GNNs) and knowledge graphs (KGs), namely Knowledge-enhanced NI (KNI).

KNI model is not only theoretically more expressive, but also achieves great improvements (1.1% ~ 8.4%) over SOTA models.
We also provide statistical analysis and case study to explain the early summarization issue and compare different models' behaviors.

For more details, please refer to our [paper](https://arxiv.org/abs/1908.04032), [poster](./material/kni_poster.pdf), [ppt](./material/kni_presentation.pdf).

### Running step-by-step

Requirements:
- python3
- numpy
- ccipy
- sklearn
- tqdm
- tensorflow-gpu

Step 1. Download the data from [https://pan.baidu.com/s/1usnQtW-YodlPUQ1TNrrafw#list/path=%2Fdataset%2Fkg4rs](https://pan.baidu.com/s/1usnQtW-YodlPUQ1TNrrafw#list/path=%2Fdataset%2Fkg4rs) or [https://drive.google.com/drive/folders/1thXezQbmuS6Q8-AXmrhB0tLM3mybJxVR?usp=sharing](https://drive.google.com/drive/folders/1thXezQbmuS6Q8-AXmrhB0tLM3mybJxVR?usp=sharing) and uncompress ``pickled_data.tar.gz`` under ``./data/``, like:

./data
        ab.pkl
        bc.pkl
        ml-1m.pkl
        ml-20m.pkl
    ./process
        *.py
    *.py

The data is processed and pickled by python3, up to 4-hop. 
According to your experiment settings, you can remove unreachable nodes and edges of the datasets.

Step 2. Run ``train.py`` with default parameters for ``bc`` dataset.
You have model options of ni (= ni:1) and ni:2, which have different attention network structures.
Knowledge graph is the default setting, and you can turn off it by setting --kg=False.

cd /path/to/code/
    python3 train.py --dataset=bc --model=ni

After a while, you will see logs like the following (the train/dev scores are disabled for speed concern):

...
    Epoch: 0057 test: auc=0.771917 ll=0.575557 acc=0.706051
    Epoch: 0058 test: auc=0.772221 ll=0.575257 acc=0.705843
    Epoch: 0059 test: auc=0.772380 ll=0.575088 acc=0.703685
    Epoch: 0060 test: auc=0.771758 ll=0.575617 acc=0.704059
    Epoch: 0061 test: auc=0.771504 ll=0.575559 acc=0.704017
    ...

The default script will run the same experiment for 5 times with different random seeds. 
You may find the experiments early stop at 0.772 AUC, 0.706 ACC (+/- 0.002).

Now you achieve the new ``state-of-the-art`` :-) (by the time of submission, the best performed model is RippleNet, 0.729 AUC, 0.663 ACC).

For the other datasets:

python3 train.py --dataset=ab --model=ni:2 --max_degree=128 --hop_n_sample=1,8 --learning_rate=1e-3 --l2_reg=1e-7 --n_eval=40 --temp=100
    python3 train.py --dataset=ml-1m --model=ni:2 --max_degree=512 --hop_n_sample=1,32 --learning_rate=1e-3 --l2_reg=1e-7 --n_eval=40 --temp=1
    python3 train.py --dataset=ml-20m --model=ni:2 --max_degree=128 --hop_n_sample=1,32 --learning_rate=1e-3 --l2_reg=1e-8 --n_eval=10 --temp=1

### Stay Connected!

If you see this paper/data/code helpful or related, please cite our paper with the following BibTeX entry

```bibtex
@inproceedings{qu2019end,
  title={An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation},
  author={Qu, Yanru and Bai, Ting and Zhang, Weinan and Nie, Jianyun and Tang, Jian},
  booktitle={Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data},
  pages={1--9},
  year={2019}
}
```

KNI

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