Abstract: The goal of this thesis is to address design and development issues in the e-commerce platform and its recommendation system. The fact that Recommendation Systems are used as part of more complex applications and affect user experience through a variety of user interfaces adds to their complexity. However, research has almost entirely focused on the ability of recommendation systems to produce accurate item rankings, ignoring the security breaches and privacy invasions that occur as a result of the use of user cache memory from various websites. We propose a new approach that uses only a small portion of the user's cache to recommend something to the user.
Keywords: Machine learning, Recommendation system, E-Commerce, Web Development
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DOI:
10.17148/IJARCCE.2023.12351
