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.

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Uncertainty Representations

Uncertainty Representations

Jessica Hullman joins us to share her expertise on data visualization and communication of data in the media. We discuss Jessica's work on visualizing uncertainty, interviewing visualization designers on why they don't visualize uncertainty, and modeling interactions with visualizations as Bayesian updates. Homepage: http://users.eecs.northwestern.edu/~jhullman/ Lab: MU Collective

4 Apr 202039min

AlphaGo, COVID-19 Contact Tracing and New Data Set

AlphaGo, COVID-19 Contact Tracing and New Data Set

Announcing Journal Club I am pleased to announce Data Skeptic is launching a new spin-off show called "Journal Club" with similar themes but a very different format to the Data Skeptic everyone is used to. In Journal Club, we will have a regular panel and occasional guest panelists to discuss interesting news items and one featured journal article every week in a roundtable discussion. Each week, I'll be joined by Lan Guo and George Kemp for a discussion of interesting data science related news articles and a featured journal or pre-print article. We hope that this podcast will give listeners an introduction to the works we cover and how people discuss these works. Our topics will often coincide with the original Data Skeptic podcast's current Interpretability theme, but we have few rules right now or what we pick. We enjoy discussing these items with each other and we hope you will do. In the coming weeks, we will start opening up the guest chair more often to bring new voices to our discussion. After that we'll be looking for ways we can engage with our audience. Keep reading and thanks for listening! Kyle

28 Mars 202033min

Visualizing Uncertainty

Visualizing Uncertainty

20 Mars 202032min

Interpretability Tooling

Interpretability Tooling

Pramit Choudhary joins us to talk about the methodologies and tools used to assist with model interpretability.

13 Mars 202042min

Shapley Values

Shapley Values

Kyle and Linhda discuss how Shapley Values might be a good tool for determining what makes the cut for a home renovation.

6 Mars 202020min

Anchors as Explanations

Anchors as Explanations

We welcome back Marco Tulio Ribeiro to discuss research he has done since our original discussion on LIME. In particular, we ask the question Are Red Roses Red? and discuss how Anchors provide high precision model-agnostic explanations. Please take our listener survey.

28 Feb 202037min

Mathematical Models of Ecological Systems

Mathematical Models of Ecological Systems

22 Feb 202036min

Adversarial Explanations

Adversarial Explanations

Walt Woods joins us to discuss his paper Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness with co-authors Jack Chen and Christof Teuscher.

14 Feb 202036min

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