Jiaxiang Li
ABSTRACT
In the scientific field of big data, creating a recommender system that effectively balances diverse user preferences represents a significant challenge. As a response to this challenge, this paper presents a comprehensive methodology to build a multi-objective recommender system using the power of machine learning, particularly focusing on the Light Gradient-Boosting Machine (LightGBM). Using the OTTO product dataset from a Kaggle competition, we delve into the process of data preprocessing, feature engineering, and model selection, in an effort to build an optimal, multi-objective recommender system. Our single-model approach, grounded in LightGBM, allows us to adeptly handle the vast and complex dataset. This method, while conceived for the OTTO competition, contributes a broader insight into the development of recommender systems that cater to multiple objectives. Through rigorous evaluation, our work demonstrates that this approach substantially enhances performance in terms of accuracy and processing speed, providing not only an effective solution for the competition's problem statement but also a significant advancement in the field of recommender system technology.
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Index Terms
- Building a Multi-Objective Recommender System Using Machine Learning Based on Light Gradient-Boosting Machine
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