Abstract
We describe in this research hybrid re-time incremental stochastic gradient descent (RI-SGD) update technology for implicit feedback factorizing (MF) systems. Implicit feedback data can be obtained more easily than explicit feedback evaluations while presenting issues for MF recommendation systems because of the transformation processes. From raw information to user preference scores. The precision of the speed of MF recommending systems is another challenge. The fresh data input grows. The proposed RI-SGD is developed for an implicit computational and accurate time-variant feedback MF recommendation system, consisting of least regular weight squares (ALSWR) alternative for training stochastic gradient (SGD) phase and descent in update phase. To show the benefits of the RI-SGD update, we implement the recommended update approaches for computational efficacy and accuracy in real-time music system of recommendations. Our numerical findings show RI-SGD methodology compared to the full-model retraining method. Almost the same recommendation accuracy can be achieved, but it only requires around 0.02% of the retraining time.
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Sharma, S., Shakya, H.K. (2022). An Efficient Hybrid Recommendation Model with Deep Neural Networks. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_36
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DOI: https://doi.org/10.1007/978-981-16-9650-3_36
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