How Does a Recommendation Engine Use Data Mining Techniques?

Recommendation engines are information filtering systems which offer appropriate recommendations based on your customer preferences. Recommendation systems are getting widely adopted across diverse sectors starting from OTT (Over-the-Top) platforms to e-commerce websites. It is important to know how a recommendation engine works in order to utilize it in the right way for your online business.

A recommendation engine or recommendation system generates relevant recommendations through the following steps-

  1. Data Mining
  2. Data Analysis
  3. Data Modelling

Data mining is the process of converting the raw data into useful information. The data mining technique is used in extracting and discovering patterns in large data sets. It has extensive application in recommendation systems, where the recommendation engines collect the data related to the actions and attributes of users. A recommendation system uses various data mining techniques such as-

  • Clustering
  • Classification Technique
  • Association Rules

And others.

Let’s discuss how a recommendation engine uses these data mining techniques to provide recommendations-


Through this data mining technique, a recommendation engine identifies the groups of users having the same preferences. These groups are called clusters. Once the clusters are created, the recommendation system can predict the preferences of an individual user who belongs to a cluster, by averaging the preference related data of the other users in the same cluster. This technique works well for a recommendation engine as the size of each cluster to be analyzed is much smaller than the whole set of users.

The leading OTT player Netflix uses clustering techniques to swiftly analyze its huge pool of data by its recommendation engine.

Classification Technique

A recommendation engine with classification technique creates classifiers at first. Classifiers are the general computational models used to assign a category to an input. The inputs for classifiers are generally the data related to the features of the products which are being classified or data about the relationships between various products to be classified. In classifier technique, a recommendation engine uses the information related to a customer and a product as inputs and generates an output category representing how strongly that product should be recommended to that customer.

For instance, if a customer prefers sports accessories and has previous purchase history of the similar items on an online store, then in this technique, the inputs to the recommendation engine will be the related customer data and a specific product. If the product is related to sports, it will be strongly recommended to that customer. If the product is something else with no purchase history, likes or positive ratings by that user, it will have a lesser chance to be recommended.

Classification technique is often used in the recommendation engines for e-commerce and various other digital stores.

Association Rules

The association rules or product-to-product correlations is a popular data mining technique used in the recommendation engines. With the help of this technique, a recommendation engine identifies the products which are frequently found in ‘association’ with the products preferred by an user. In order to know the user preference, the related data such as ratings, thumbs ups or downs, feedback, etc. are used. If a user likes watching sci-fi series over an OTT platform, then the series of similar genre will be recommended to that user by this data mining technique.

The e-commerce platform Amazon uses a recommendation engine with association rules to recommend relevant products to its users.

Data mining is crucial for any recommendation engine, be it collaborative, content-based or hybrid one. Not only it enables a recommendation engine in suggesting relevant products but, temporal recommenders use data mining techniques in order to suggest the time a recommendation should be made to an user or when an user is likely to consume a product.

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