LensKit for Python: Next-Generation Software for Recommender System Experiments
TL;DR: The next generation of the LensKit project is presented, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development.
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Abstract: LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deployments, and education in both MOOC and traditional classroom settings. In this paper, I present the next generation of the LensKit project, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development. LensKit for Python (LKPY) enables researchers and students to build robust, flexible, and reproducible experiments that make use of the large and growing PyData and Scientific Python ecosystem, including scikit-learn, TensorFlow, and PyTorch. To that end, it provides classical collaborative filtering implementations, recommender system evaluation metrics, data preparation routines, and tools for efficiently batch running recommendation algorithms, all usable in any combination with each other or with other Python software.
This paper describes the design goals, use cases, and capabilities of LKPY, contextualized in a reflection on the successes and failures of the original LensKit for Java software.
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Citations
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TL;DR: The FEVR framework provides a structured foundation to adopt adequate evaluation configurations that encompass this required multi-facettedness and provides the basis to advance in the field.
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Exploring author gender in book rating and recommendation
TL;DR: It is found that common collaborative filtering algorithms tend to propagate at least some of each user’s tendency to rate or read male or female authors into their resulting recommendations, although they differ in both the strength of this propagation and the variance in the gender balance of the recommendation lists they produce.
Evaluating Stochastic Rankings with Expected Exposure
TL;DR: In this paper, the authors introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query, and advocate for the adoption of the principle of equal expected exposure.
Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison
Amifa Raj,Michael D. Ekstrand +1 more
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TL;DR: This paper describes several fair ranking metrics from the existing literature in a common notation, enabling direct comparison of their approaches and assumptions, and empirically compare them on the same experimental setup and data sets in the context of three information access tasks.
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