Mab2Rec (AAAI'24) 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. The library is designed with rapid experimentation in mind, follows the PEP-8 standards, and is tested heavily.
Mab2Rec is built on top of several other open-source software developed at the AI Center at Fidelity:
- MABWiser to create multi-armed bandit recommendation algorithms (Bridge@AAAI'24, TMLR'22, IJAIT'21, ICTAI'19).
- TextWiser to create item representations via text featurization (AAAI'21).
- Selective to create user representations via feature selection (AMAI'24, CPAIOR'21, DSO@IJCAI'21).
- Seq2Pat to create user representations via sequential pattern mining (AI Magazine'23, AAAI'22, Bridge@AAAI'23, Frontiers'22, KDF@AAAI'22, CMU Blog Post)
- Jurity to evaluate recommendations including fairness metrics (ACM'24, LION'23, CIKM'22, ICMLA'21).
An introduction to content- and context-aware recommender systems and an overview of the building blocks of the library is presented at AAAI 2024 and All Things Open 2021. There is a corresponding blogpost to serve as a starting point for practioners to build and deploy bandit-based recommenders using Mab2Rec.
Documentation is available at fidelity.github.io/mab2rec.
Mab2Rec supports prototyping with a single bandit algorithm or benchmarking with multiple bandit algorithms. If you are new user, the best place to start is to experiment with multiple bandits using the tutorial notebooks.
# Example of how to train an singler 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')# Example of how to benchmark multiple recommenders 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")We provide extensive tutorials in the notebooks folder with guidelines on building recommenders, performing model selection, and evaluating performance.
- Data Overview: Overview of data required to train recommender.
- Feature Engineering: Creating user and item features from structured, unstructured, and sequential data.
- Model Selection: Model selection by benchmarking recommenders using cross-validation.
- Evaluation: Benchmarking of selected recommenders and baselines on test data with detailed evaluation.
- Advanced: Demonstration of advanced functionality such as persistency, eligibility, item availability, and memory efficiency.
Mab2Rec requires Python 3.8+ and can be installed from PyPI using pip install mab2rec or by building from source as shown in installation instructions.
If you use Mab2Rec in a publication, please cite it as:
@inproceedings{DBLP:conf/aaai/KadiogluK24,
author = {Serdar Kadioglu and Bernard Kleynhans},
title = {Building Higher-Order Abstractions from the Components of Recommender Systems},
booktitle = {Thirty-Eighth {AAAI} Conference on Artificial Intelligence, {AAAI} 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2014, February 20-27, 2024, Vancouver, Canada},
pages = {22998--23004},
publisher = {{AAAI} Press},
year = {2024},
url = {https://doi.org/10.1609/aaai.v38i21.30341},
doi = {10.1609/AAAI.V38I21.30341}
}Please submit bug reports and feature requests as Issues.
Mab2Rec is licensed under the Apache License 2.0.