A recommendation engine or recommendation system has the ability to predict which products or services a user will prefer. Most of the online businesses starting from e-commerce stores to OTT channels use recommendation engines. ‘Recommended Movies’ on Amazon Prime, ‘Bought Together’ on Alibaba, and ‘You might also like’ on Amazon are some of the examples of using recommendation engines.
Here we have summarized the functions of a recommendation engine-
Data Collection
A recommendation system is an information filtering tool which collects various data related to the users and products or services. Then it analyzes those to recommend the preferred products or services to the users. These data include-
- User Behavioral Data
The user behavioral data represent the preferences of the users for various products. Likes, dislikes, ratings, reviews, feedback, etc. are the examples of user behavioral data. User behavioral data are collected from user purchase history, search history, clicks, and others by a recommendation engine.
- User Demographic Data
The user demographic data plays an important role to generate precise recommendations for the users with diverse demographics. The user demographic data includes age, gender, race, income, education etc. These data are often collected from the user profile.
- Product Attribute Data
Product attribute data are as crucial as user data to yield accurate recommendations. Some examples of product attribute data are- color of shoes, genre of movies, singer name of music, etc.
Data Analysis and Providing Recommendations
Once the data are collected, the function of a recommendation engine is to analyze them and provide the relevant recommendations to each of the users. A recommendation engine uses three types of filtering techniques to generate Recommendations.
- Collaborative Filtering
In collaborative filtering, a recommendation system recommends a user the products on the basis of the preferences of the other users with similar tastes. For instance, if a user loves to listen to pop music on Spotify and the other users with similar preferences love to listen to pop and rock music, then rock music too will be recommended to that user.
- Content Based Filtering
Content based filtering focuses on the features of the content. For instance, if you like shopping red dresses on Amazon, it will recommend other dresses of the same color. In the same way, if you prefer watching action movies on Netflix, then other action movies or series will be recommended to you.
- Hybrid Filtering
Hybrid filtering is the combination of both collaborative and content based filtering. Most of the leading online businesses such as Netflix, Amazon, Hulu, Alibaba, etc. use recommendation engines with hybrid filtering.
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