mab2rec 1.0.1

Mab2Rec: Multi-Armed Bandits Recommender

Mab2Rec: Multi-Armed Bandits Recommender

Mab2Rec is a Python library for building bandit-based recommendation algorithms. It supports context-free, parametric and non-parametric contextual bandit models powered by MABWiser and fairness and recommenders evaluations powered by Jurity. It supports all bandit policies available in MABWiser. The library is designed with rapid experimentation in mind, follows the PEP-8 standards and is tested heavily.

Mab2Rec and several other open-source software it is built on is developed by the AI Center at Fidelity:

• MABWiser to create multi-armed bandit recommendation algorithms (IJAIT'21, ICTAI'19).
• TextWiser to create item representations via text featurization (AAAI'21).
• Selective to create user representations via feature selection.
• Seq2Pat to enhance users representations via sequential pattern mining (AAAI'22).
• Jurity to evaluate recommendations including fairness metrics (ICMLA'21).
• Spock to define, manage, and use parameter configurations.

An introduction to content- and context-aware recommender systems and an overview of the building blocks of the library is presented at All Things Open 2021.

Documentation is available at fidelity.github.io/mab2rec.

Usage Patterns

Mab2Rec supports prototyping of individual bandit algorithms and benchmarking of multiple bandit algorithms. The intended usage for a new user is to experiment with multiple bandits using the tutorial in the notebooks folder.

Quick Start

Individual Recommender

# Example of how to train an individual recommender to generate top-4 recommendations

# Import 
from mab2rec import BanditRecommender, LearningPolicy
from mab2rec.pipeline import train, score

# LinGreedy recommender to select top-4 items with 10% random exploration 
rec = BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1), top_k=4)

# Train on (user, item, response) interactions in train data using user features 
train(rec, data='data/data_train.csv', 
      user_features='data/features_user.csv')

# Score recommendations for users in test data. The output df holds 
# user_id, item_id, score columns for every test user for top-k items 
df = score(rec, data='data/data_test.csv', 
           user_features='data/features_user.csv')

Multiple Recommenders

# Example of how to benchmark multiple bandit algorithms to generate top-4 recommendations

from mab2rec import BanditRecommender, LearningPolicy
from mab2rec.pipeline import benchmark
from jurity.recommenders import BinaryRecoMetrics, RankingRecoMetrics

# Recommenders (many more available)
recommenders = {"Random": BanditRecommender(LearningPolicy.Random()),
                "Popularity": BanditRecommender(LearningPolicy.Popularity()),
                "LinGreedy": BanditRecommender(LearningPolicy.LinGreedy(epsilon=0.1))}

# Column names for the response, user, and item id columns
metric_params = {'click_column': 'score', 'user_id_column': 'user_id', 'item_id_column':'item_id'}

# Performance metrics for benchmarking (many more available)
metrics = []
for top_k in [3, 5, 10]:
    metrics.append(BinaryRecoMetrics.CTR(**metric_params, k=top_k))
    metrics.append(RankingRecoMetrics.NDCG(**metric_params, k=top_k))

# Benchmarking with a collection of recommenders and metrics 
# This returns two dictionaries; 
# reco_to_results: recommendations for each algorithm on cross-validation data
# reco_to_metrics: evaluation metrics for each algorithm
reco_to_results, reco_to_metrics = benchmark(recommenders,
                                             metrics=metrics,
                                             train_data="data/data_train.csv",
                                             cv=5,
                                             user_features="data/features_user.csv")

Usage Examples

We provide an extensive tutorial in the notebooks folder with guidelines on building recommenders, performing model selection, and evaluating performance.

1. Data Overview: Overview of data required to train recommender.
2. Feature Engineering: Creating user and item features from structured, unstructured, and sequential data.
3. Model Selection: Model selection by benchmarking recommenders using cross-validation.
4. Evaluation: Benchmarking of selected recommenders and baselines on test data with detailed evaluation.
5. Advanced: Demonstration of advanced functionality.

Installation

Mab2Rec can be installed from PyPI using pip install mab2rec or by building from source by following the instructions in our documentation.

Support

Please submit bug reports and feature requests as Issues.

License

Mab2Rec is licensed under the Apache License 2.0.