Abstract
With the rise of the Web and technological developments, the amount of data to use or analyse has become very large. It has therefore become difficult to know what data to look for and where to find it. This problem has contributed to the establishment of recommendation systems that allow users to access relevant resources as quickly as possible according to their preferences. Collaborative Filtering (CF) is the most well-known technique for recommendation. CF technique uses the user’ behaviour in form of user-item ratings, as their information source for prediction. This article presents a comparison study between two methods of collaborative filtering: the Singular Value Decomposition (which is considered as the most powerful matrix factorization technique to reduce dimensionality) and deep multilayer perceptron (is a class of feedforward artificial neural network, it can add the non-linear transformation to existing recommendation system approaches and interpret them into neural extensions). Both systems are evaluated on a dataset with metrics: recall at top k, NDCG@k.
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El Alaoui, D., Riffi, J., Aghoutane, B., Sabri, A., Yahyaouy, A., Tairi, H. (2021). Collaborative Filtering: Comparative Study Between Matrix Factorization and Neural Network Method. In: Georgiou, C., Majumdar, R. (eds) Networked Systems. NETYS 2020. Lecture Notes in Computer Science(), vol 12129. Springer, Cham. https://doi.org/10.1007/978-3-030-67087-0_24
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DOI: https://doi.org/10.1007/978-3-030-67087-0_24
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