pyRecLab: A Software Library for Quick Prototyping of Recommender Systems
원문 초록
This paper introduces pyRecLab, a software library written in C++ with Python bindings which allows to quickly train, test and develop recommender systems. Although there are several software libraries for this purpose, only a few let developers to get quickly started with the most traditional methods, permitting them to try different parameters and approach several tasks without a significant loss of performance. Among the few libraries that have all these features, they are available in languages such as Java, Scala or C#, what is a disadvantage for less experienced programmers more used to the popular Python programming language. In this article we introduce details of pyRecLab, showing as well performance analysis in terms of error metrics (MAE and RMSE) and train/test time. We benchmark it against the popular Java-based library LibRec, showing similar results. We expect programmers with little experience and people interested in quickly prototyping recommender systems to be benefited from pyRecLab.
함께 살펴볼 논문
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Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
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