Objectives Develop an algorithm to find similar customers for an online store Implement the algorithm with the best runtime complexity available Scenario Your team is working on an online shopping website that has been receiving increased traffic over the last few months due to the introduction of new products and an increase in popularity. The growth in the number of visitors has resulted in the website becoming slower with each passing day. This is mainly because the algorithms used to design the initial website have a high runtime complexity, which worked well when the numbers of customers were low, but with the growth of the business, the website needs well-developed algorithms with a low runtime complexity to handle the dramatic increase in workload. Figure 1.1 shows what a typical product page on the website looks like. It contains two auto-generated parts, which are shaded in purple in the figure. These parts load separately from the main page because they are generated by algorithms that utilize available information on the current customer and behaviors of previous customerinformation that is obtained by logging the behaviors of customers in a customer behavior database. Initially, these auto-generated parts loaded with the remaining content. However, due to the expansion of the business, the auto-generated content now loads several seconds after the remaining content. This decreases the user experience and makes the website unprofessional. The company, therefore, wants a faster algorithm that will analyze the customer behavior database, find similar customers, and provide the user with relevant suggestions as soon as their webpage loads.