For as long as our species has been around, we’ve been shopping. The first time someone made a purchase for the purpose of consuming what another person was selling was probably about 2,000 BCE in Mesopotamia. However, it wasn’t until the rise of online shopping that we saw an explosion in how people shopped across the globe. In fact, today e-commerce accounts for more than 8% of total retail sales globally and is projected to reach 12% by 2025 (compared with 7% in 2017).
While online shopping is certainly convenient, it’s also rife with challenges: navigating websites can be difficult. Products are often difficult to find or compare; prices aren’t always clear up front. Recognizing this challenge, many retailers have begun using product recommendation engines to help customers make better choices about. Which items they’ll ultimately buy—which makes sense considering shoppers often cite recommendations from friends as one reason. Why are they more likely to purchase something online rather than at a physical store location? Where they could touch or try something beforehand first hand.
Product recommendation engines are a type of recommender system, which is a type of technology that uses data and algorithms to predict what users might want. They help online retailers improve the shopping experience by showing relevant products to their customers.
Product recommendation engines work by analyzing user behavior such as what they’ve viewed before. How long they spent on certain pages and where they clicked on links in those pages. These insights are then used to recommend similar products that may be of interest based on past purchases or browsing habits.
Product recommendation engines use data to recommend products. Data is collected from users, products and other sources, which is used to generate recommendations. Recommendations are generated using machine learning models such as collaborative filtering or reinforcement learning.
The first challenge is to build a product recommendation engine. This can done through either supervised or unsupervised learning methods, which we will discuss in more detail later on. The second challenge is to train the engine so that it make accurate predictions about what items willpurchased together, based on historical data collected from previous purchases and other sources of information like reviews and social media posts.
The third challenge involves testing your model and understanding its limitations before deploying it in production–a process called validation (you’ll learn more about this later). Finally, once you’ve validated your model and are ready for deployment, there are many challenges related to scaling up your system so that it can handle large amounts of data without losing accuracy or performance over time as new users join the platform!