DataRec Library for Reproducible in Recommend Systems

DataRec Library for Reproducible in Recommend Systems

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems research. Guest Alberto Carlo Maria Mancino, a postdoc researcher from Politecnico di Bari, Italy, discusses the challenges of dataset management in recommendation research—from version control issues to preprocessing inconsistencies—and how DataRec provides automated downloads, checksum verification, and standardized filtering strategies for popular datasets like MovieLens, Last.fm, and Amazon reviews.

The conversation covers Alberto's research journey through knowledge graphs, graph-based recommenders, privacy considerations, and recommendation novelty. He explains why small modifications in datasets can significantly impact research outcomes, the importance of offline evaluation, and DataRec's vision as a lightweight library that integrates with existing frameworks rather than replacing them. Whether you're benchmarking new algorithms or exploring recommendation techniques, this episode offers practical insights into one of the most critical yet overlooked aspects of reproducible ML research.

Episoder(589)

Forecasting Motor Vehicle Collision

Forecasting Motor Vehicle Collision

Dr. Darren Shannon, a Lecturer in Quantitative Finance in the Department of Accounting and Finance, University of Limerick, joins us today to talk about his work "Extending the Heston Model to Forecast Motor Vehicle Collision Rates."

20 Des 202139min

Deep Learning for Road Traffic Forecasting

Deep Learning for Road Traffic Forecasting

Eric Manibardo, PhD Student at the University of the Basque Country in Spain, comes on today to share his work, "Deep Learning for Road Traffic Forecasting: Does it Make a Difference?"

13 Des 202131min

Bike Share Demand Forecasting

Bike Share Demand Forecasting

Daniele Gammelli, PhD Student in Machine Learning at Technical University of Denmark and visiting PhD Student at Stanford University, joins us today to talk about his work "Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management."

6 Des 202140min

Forecasting in Supply Chain

Forecasting in Supply Chain

Mahdi Abolghasemi, Lecturer at Monash University, joins us today to talk about his work "Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion."

29 Nov 202136min

Black Friday

Black Friday

The retail holiday "black Friday" occurs the day after Thanksgiving in the United States. It's dubbed this because many retail companies spend the first 10 months of the year running at a loss (in the red) before finally earning as much as 80% of their revenue in the last two months of the year. This episode features four interviews with guests bringing unique data-driven perspectives on the topic of analyzing this seeming outlier in a time series dataset.

26 Nov 202144min

Aligning Time Series on Incomparable Spaces

Aligning Time Series on Incomparable Spaces

Alex Terenin, Postdoctoral Research Associate at the University of Cambridge, joins us today to talk about his work "Aligning Time Series on Incomparable Spaces."

22 Nov 202133min

Comparing Time Series with HCTSA

Comparing Time Series with HCTSA

Today we are joined again by Ben Fulcher, leader of the Dynamics and Neural Systems Group at the University of Sydney in Australia, to talk about hctsa, a software package for running highly comparative time-series analysis.

15 Nov 202142min

Change Point Detection Algorithms

Change Point Detection Algorithms

Gerrit van den Burg, Postdoctoral Researcher at The Alan Turing Institute, joins us today to discuss his work "An Evaluation of Change Point Detection Algorithms."

8 Nov 202130min

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