Recommender system for casino
The recommender system predicts in which games a user might want to play in an online casino.
Algorithm
The system uses a hybrid weighted model that consists of 3 inner algorithms.
where ri is an inner recommender and ci is the corresponding coefficient.
This logic is implemented in HybridRecommender.
All recommenders are memory-based. They use a cosine-similarity matrix. All of them use CosineRecommender that computes predictions using cosine-similarity matrix.
Collaborative-filtering recommender
It uses user-item matrix (where m[u, g]=k means that user u played in game g k times)
to compute an item-item similarity matrix.
Data preprocessing for this part can be found in user_data_preprocessing.py
Content-based recommenders
There are 2 content-based recommenders. The first is based on data from master_game_features.csv
and the second is based on data from game_feature_derived_enc.csv.
They have pretty similar logic when data from corresponding csv files are used
to compute an item-item similarity matrix.
Data preprocessing for this part can be found in game_data_preprecessing.py
Set up
- Install Python 3.8
- Create and activate virtual environment
pip install -U -r requirements.txt- Configure data paths and other settings in
config.yml - Run training via
python train_pipeline.py
Training
The whole training pipeline can be found in train_pipeline.
An example of searching coefficients for the hybrid model can be found in train_eval.
Evaluation
Test dataset creation
- Split users on test and train parts
- For each user in the test part treat
noldest events as "features" and the rest events as "labels" - Create user-item matrix using "feature" events
Metrics calculation
- Compute prediction using user-item matrix from the previous part.
- Compute
Precision@Kmetric using "labels" part from the previous part.
Metrics
0.457 Precision@5 best result from train_eval