Abstract
Matrix factorization (MF), dimensional reduction techniques are broadly used in recommender systems (RS) to retrieve the preference of user from explicit ratings. However, the interactions are not always consistent due to the influence of numerous elements on users on a product, including friend’s recommendation and business publicizing. In comparison, traditional MF is not able to find consistent ratings. Find the exact prediction/ratings of a product/item is essential for further improvement of the performance of the collaborative recommender framework. To find the exact prediction, we propose the parameter optimizing stochastic gradient descent (SGD) and alternate least square (ALS) over MF. Furthermore, we examine the deviation of prediction error after setting each parameter over a general parameter distribution of both techniques (SGD and ALS). To evaluate the performance of the proposed model, we use two well-known datasets. The exploratory outcomes reveal that our approach gets significant improvement over the base model.
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Behera, G., Nain, N. (2021). Collaborative Recommender System (CRS) Using Optimized SGD - ALS. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_55
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