Abstract
Recommender systems are essential to many of the largest internet companies’ core products. Today's online users expect sites offering a vast assortment of products to provide personalized recommendations. Although traditional recommender systems optimize for prediction accuracy, such as RMSE, they often fail to address other important aspects of recommendation quality. In this paper, we explore the crucial issue of diversity in the recommendations generated by recommender systems. We explain why diversity is essential in recommender systems and review related work on diversifying recommendations. We quantify and classify various diversity metrics into logical categories. Then, we introduce Pyrorank, a novel bio-inspired re-ranking algorithm designed to improve recommendation diversity. Pyrorank is inspired by the positive effects of pyrodiversity in nature and is optimized to increase user-based diversity and mitigate the systemic bias that traditional recommender system models learn from the data. Our experimental results on multiple large datasets indicate that Pyrorank can achieve better user-based diversity metrics than state-of-the-art re-ranking methods, with little decrease in prediction accuracy.
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Acknowledgements
We would like to thank Mengting Wan and Julian McAuley for the new Goodreads dataset and responding promptly to our emails about the dataset.
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Kilitcioglu, D., Greenquist, N., Bari, A. (2023). Pyrorank: A Novel Nature-Inspired Algorithm to Promote Diversity in Recommender Systems. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13969. Springer, Cham. https://doi.org/10.1007/978-3-031-36625-3_12
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