In the following picture, when we select the book Programming in Scala at Amazon, we see suggestions (or recommendations) for those books that were brought together with the selected book:

The following is another example of product recommendations based on the frequent patterns, that is, the products that are bought together by customers. Here Moto E mobile phone and Moto E back cover combo are recommended for the user to buy.

We can also determine that if two people like similar items, then if one person liked/bought something that other person didn't, we can recommend it. The following are some examples based on people to people correlation. See the header Customers Who Bought This Item Also Bought.

Today e-commerce sites are so popular that we tend to buy as much as we can online because it saves us time and energy. However, our decision to buy a product greatly depends on the reviews and ratings provided by other customers. Check out the Customer Reviews and Most Helpful Customer Reviews sections in the following screenshot:

Notice how the customers' aggregated rating (stars) and their distribution across five different ratings is shown. Also note that customer reviews are listed together, making it very easy for a user to make decisions.

In the preceding screenshot, we see no reviews for a given product; however, that space is complemented by recommendations based on the user's recent history. Basically, we fill in the space with some information that could help a user find relevant items.

We can use the search results of other users who have also searched for similar products and provide it to our current user. This will give the user a larger variety of options in products and also keep him/her interested in the site. The following screenshot illustrates this:

We can also add another dimension to the mix, that is we can recommend based on the current activity of all the users. The following picture demonstrates that on selecting a Dell laptop on eBay we see what laptops people are currently watching. This makes sense in those situations where we have a lot of people currently making transactions, and the lifetime of a particular item on the website is pretty short. This is true of consumer to consumer sites. On such sites, people auction an item, and it is sold in a very short time of maybe hours, days, or weeks. However, on many other e-commerce sites, the items would be available as long as they are available across the markets.

In the following screenshot, we see similar news items, which are placed right beside the current news item. This is an example of item to item correlation:

In the following table, we can summarize the three components of a recommender system. Notice how the recommendation technology is annotated with appropriate input data, below it. Also note that this is only a selective coverage of these websites, not an exhaustive coverage. There may be many more features that are not immediately obvious.

There are two more metrics we need to discuss. First is the effort put by a user in actually reaching to a recommendation on a scale of manual to automatic. Second is the persistence of recommendations on the scale of ephemeral to persistent.