A recommendation engine (or system) is an algorithm that analyzes the user behavior to suggest items which they are likely to prefer. A recommendation system uses data analysis techniques to figure out the items that match the users' taste & preferences. The ultimate aim of any recommendation engine is to stimulate demand and engage users.
Recommendation engines can have many use cases like in entertainment, e-commerce, mobile apps, education, etc. In general, a recommendation engine can come in handy wherever there is a need to give personalized suggestions and advice to users.
By using a recommendation engine you can provide personalized suggestions to buyers and improve catalog visibility. One of the reasons why in-store retail is still relevant is because in a physical store the salesman understands the buyer and provides personalized suggestions. If you like a particular style of t-shirt then the salesperson will showcase more of the similar kind. The reason why we trust our friends and family for advice is that they understand us. We don’t have to tell them every time what we need, they just know it. By using a recommendation engine you can earn a similar level of trust. Thus making long-term loyal customers which are essential for every business.
A recommendation engine can work in various ways. Depending upon your business model and needs you can create one in either of these 3 ways:
In case of collaborative filtering, the recommendations are placed on the basis of buyer’s history. For example, in case of Facebook and LinkedIn, the person would get suggestions based on mutual friends and connections. If you like something then you will get recommendations based on the behavior of people with similar demographics.
While this is a very effective approach when applied to scale, there are some minor limitations to it. To implement a collaborative filtering recommendation engine you need a good amount of customer data (i.e. many users) so that you can identify trends and find people who show similar behavior.
In content-based filtering, certain attributes and keywords are attached to each item. Based on what people like or dislike the attributes are given weights. A profile is created for every user and his likes and dislikes for different attributes is recorded. The items are recommended if their attributes match the profile of the user.
This approach is less effective since it difficult to attach attributes to items and the recommendations turn out to be vague. Another issue with this approach is the inability to map products which are not similar to each other.
