Review of Recommendation System Methodologies
DOI:
https://doi.org/10.61841/sc5r5j51Keywords:
Customization, Audio Recognition, Deep Learning.Abstract
Recommender systems became extraordinarily common in recent years. Companies, such as Amazon or eBay, developed an outsized variety of products to fulfill totally different desires of customers. There is an increasing variety of choices, measured out to the customers. Thus, during this new level of customization, so as to search out what they actually need, customers should frame a model or method from an outsized quantity of data provided by businesses. One answer to ease this drawback is recommender systems. On one hand, traditional systems recommend things supported by totally different criteria, such as the past preferences of users or user profiles. On the other hand, deep learning techniques deliver the goods promising performance in numerous areas, like PC vision, audio recognition, and language processing. However, applications of deep learning in recommender systems haven't been well explored. Many progressive deep recommendation systems are discussed in this analysis.
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