In the consumer specific markets today, businesses need to stay a step ahead of the customer in order to flourish. Recommendations use the data that the customers generate, so that you can analyze patterns and behavior that can be used to suggest products, people, or point of sales. Over the years, several techniques have been developed to generate recommendations. Let us take a look at the major approaches:
It is one of the most common techniques that recommendation engines today are based on. Collaborative Filtering refers to the method of pattern or information filtering with collaboration between various data sources, viewpoints, agents, etc. It uses previous or historical data of a user, or other users, to profile a pattern, and then uses it to predict what other content the user might like.
Let us take an example to understand this. On e-commerce websites, you are presented with suggestions for products that you might buy, based...