Last edit : 29 July 2019

Recommender system referencing KPRN, original github, trained using custom MovieLens-20M dataset.

The model implemented has slight difference where no pooling layer added at the end of LSTM.

Given a path between user and an item, predict how likely the user will interact with the item

• /cache : temporary files used in training

• /data : contains dataset (custom ml-20m dataset where only movies shows up in Ripple-Net's knowledge graph used) used in training.

• /log : contains training result stored in single folder named after training timestamp.

• /test : contains jupyter notebook used in testing the trained models

• KPRN-LSTM.ipynb : notebook to train model

• main.py : python3 version of KPRN-LSTM.ipynb

*italic* means this folder is ommited from git, but necessary if you need to run experiments
**bold** means this folder has it's own README, check it for detailed information :)

pip3 install -r requirements.txt

1. Unzip ratings_re.zip and ratings_re.z01 in /data

2. To preprocess, run Preprocess.ipynb notebook or preprocess.py

   python3 data/preprocess.py
   

3. To train, run KPRN-LSTM.ipynb notebook or main.py

   python3 main.py
   

To start using new dataset, or if you wish to generate new dataset, please delete all items inside /cache

Open KPRN-LSTM.ipynb or main.py and change the model parameters

1. Find the training result folder inside /log (find the latest), copy the folder name.
2. Create copy of latest jupyter notebook inside /test folder.
3. Rename folder to match a folder in /log (for traceability purpose).
4. Replace TESTING_CODE at the top of the notebook.
5. Run the notebook

KPRN relies heavily upon paths, and those paths are handcrafted by using the knowledge-graph. The paths are also sampled from hundreds of million possible paths.

• To find paths

  (user -> seed item (eg: Castle on The Hill) -> entity (eg: Ed Sheeran) -> suggestion (eg: Perfect)) 
  

  from each seed, we can extract millions of paths (if not sampled), even after sampled using only one relation (eg: same artist, same albums, etc per seed, it still generates around 8k-10k path per seed.

• Each user has multiple item work as seed (typically 20+), this need to be sampled again to reduce paths generated and reduce computational cost.

• We do make sure each item in suggestion has about 4 - 7 paths

• At this point, we only evaluate on around 75 - 150 path per user, out of possible hundred million possible paths

• That's a huge possible source of sampling bias, but at the same time, it's kinda impossible to search through all paths.

• Looking from the result of KPRN, the usage of KG might turn out to be quite promising, especially to improve the diversity of suggestion.

• The downside of using KPRN is that the result rely heavily on 'handcrafted' paths, which undergoes a lot of downsampling steps.

• Summary compared to non-KG RecSys: Big improvement in terms of Prec@k and distinct rate

• Able to incorporate Knowledge Graph as another source of information
• Able to infer why a user is given such suggestions (based on path scores)
• Able to adjust between 'exploration and optimization' by applying result pooling (the model doesn't require to be re-trained)
• During training, the model converge really fast (< 10 epochs)

• No original implementation usable
• Relies heavily upon paths, and those paths are 'handcrafted' by using the knowledge-graph and also sampled from hundreds of million possible paths.
• Huge possible sampling bias introduced from preprocessing step and path generation step.
• The model remember the user, the model need to be re-trained for every new user and item addition.
• Require relatively slow preprocessing
• Super slow train and prediction time
• Loss function and metric used in training is not Prec@K, instead it uses accuracy.

• At the cost of slightly different implementation, it's easier to implement using high-level libraries such as Keras instead of using original version.
• path generation & predict time : about 4k path / second
• different sampling method and sampling parameter has insignificant effect
• Using more items as path generation 'seed' (for predicting suggestion), should lead to more personalized suggestions. (i.e. consider the suggestion by using more user history)
• By pooling path prediction-score for the same items, the model should be able to give a better suggestion since it considers multiple reasons instead of just a single reason.

• Jessin Donnyson - [email protected]

• Michael Julio - [email protected]
• Fallon Candra - [email protected]