Abstract
To increase recommendation accuracy we utilize multiple individual-item models instead of a single multi-classification model. Retraining all multiple models consumes significant time. To selectively retrain only models where new and historical consumers differ, we estimate the distance between historical and new consumers including both categorical and numerical consumer attributes to determine when to retrain each of the recommendation models.
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The authors thank Zhongzheng Zach Shu for his help with the model implementation.
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Peran, M., Augenstern, D., Price, J., Nahar, R., Srivastava, P. (2020). Optimizing Retraining of Multiple Recommendation Models. In: Zhang, J., Dresner, M., Zhang, R., Hua, G., Shang, X. (eds) LISS2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-5682-1_3
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DOI: https://doi.org/10.1007/978-981-15-5682-1_3
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