# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 5 Business Adoption and Applications
### Recommendation Systems in e-Commerce  
RSs are aimed at providing customized recommendations of products to customers of websites. They learn from the customer and recommend relevant products to the user. These systems personalize the experience while attaining user interest. Simply, there are three ways a product is recommended based on (i) top overall sellers on a site, (ii) the demographics of the customer, or (iii) the previous purchase history of the customer. Implementing a RS personalizes a site as they adapt and are unique to each customer. On the other hand, RSs can enhance sales of e-commerce sites in three ways.  
\(\bullet\)_Browsers into buyers_: Visitors to an e-commerce site often look over products without buying anything, but if a site displays relevant recommendations to a user, they are more likely to purchase.  
\(\bullet\)_Cross-sell_: Recommendation techniques suggest additional products to the users, apart from the one they are already buying. With this, the average order size should increase over time  
\(\bullet\)_Loyalty:_ In an era where a competitor's site can be visited by a mere click or two, loyalty is essential. RSs personalize the site for each user, which builds the user-site relationship. The more a customer uses a system, the more they are training the system, the more loyal a customer becomes, which also improves the quality of recommendations, over time.  
RSs are also used as a technology to enable businesses to target customers and make offers to them. For instance, search engines and advertising companies rely on showing effective suggestions to users based on their behavior. Different recommendation methods have been used to achieve these goals, such as statistical methods, raw retrieval, attribute-based methods, user-to-user, or item-to-item correlation techniques. Each process may require different input types such as customer purchase history or information from users' communities, such as their ratings to items [4]. These methods usually result in providing prediction, suggestion, or ratings of items. Some applications of RSs in e-commerce businesses based on [4] are on Amazon.com, Drugstore.com, CDNOW (Compact Discs Now), eBay, Reel.com, and MovieFinder.com. Amazon.com has been noticed by more researchers [5] because of its highly personalized website and its launch of item-based collaborative filtering, which is one of the commonly used types of RSs. YouTube and Netflix have used collaborative filtering for video and movie recommendations. There are also some criticisms of the collaborative filtering method as it can be poorly be justified because it solely depends on rating data and disregarding the content data [53]. With the increasing number of RSs in e-commerce, companies need to select the best recommendation algorithm; therefore, [PERSON] et al. [54] has proposed a framework to choose an optimal collaborative filtering algorithm. They have used K-nearest Neighbour (KNN) as a classification method for this purpose and tested the result on two real datasets of women's clothing and furniture. A recent study [55] provides a user interface for an e-commerce website based on users' behavior using a deepneural network method. Their analysis revealed the effect of a website layout to recommended items based on the user's behavior. Customers reviews are used widely in RSs; in [56], A recent survey focuses on sentiment analysis on text reviews. They have illustrated that, generally, there are three types of RSs using text reviews based on words, topics, and opinions. Even though RSs have been utilized in e-commerce for a long time, there are still challenges in this area. For example, there is the issue of scalability and data latency. A RS on a website with a large number of users should respond in real-time. Also, there is a data sparsity issue of rating datasets as not all the customers will rate all the products.