Recommendor System

You would have often wondered why your social media handles sometimes promote advertisements which look much similar to what you recently surfed on the Internet about.
You might have been watching some K-Pop videos on your YouTube and suddenly your Instagram account recommendations would be full of K-pop related content.

The list of suggestions on all the platforms which are related to the recent view of yours is the work done by the recommendor system. Recommendor system is often termed as recommendation engine or recommendation system. This system offers automatic personalization of individuals’ likes and dislikes based on their repeated activities on the web. The user would be recommended certain videos and sponsorships based on their behaviour.

The recommendation engine determines an Individual’s personal taste based on their view which sometimes not really be accurate. Many a times recommendation engine allows users to look at content that they have never heard of but the content would somehow be related to their previous view.

Recommendation engine is very useful to suggest products or sponsored advertisements to the target customers based on their recent activities.
For instance, you would have surfed about a new brand mobile phone and all the websites that you open will have pop up advertisements which root many other similar devices for the customer.

Types of Recommendation Engine:

• Content-Based
• Collaborative filtering • Knowledge-Based
• Deep Neural Network

Content-Based Recommendor:

Content-based recommendor will use the attribute of a product which a user recently checked upon and recommend similar products to that user. The recommendor will rely on the past behaviour of the user to promote.

For example, a user who watches rom-com movies in Netflix will be recommended top rated rom-com movies when the user logs in back. This recommendation system will be working based on the language, the region the customer has repeatedly watched from.

Another instance, if a customer checks upon mobile phones in many sites, they will be automatically recommended about other mobile phones or things which are related like Adapter, USB cables, cases etc.,

Collaborative Filtering:

Collaborative filtering does not necessarily use any attribute to recommend certain products to a user. This type of recommendation engine relies upon the ratings/reviews of many users from a particular place.

Recommendation engine will set target on particular audience who belong to the region or language where the system has gathered information about.

For example, the recommendation system will recommend Tamil movies to a user from Tamil Nadu based on the ratings and reviews given on that particular movie by other users who live in Tamil Nadu.

Knowledge-Based:

The knowledge-based recommendor will provide knowledge about a certain product which other users would have check upon. This system conceives all database to solve complex issues faced by users.

For example, a customer who views about a gadget on a site with a particular question on their mind, say regarding the storage space will be provided with the answer on the main page because that would have been the question prioritised by other customers.

Deep Neural Network:

Deep neural network is none other than a combination of content based, collaborative filtering, knowledge-based recommendation systems. This recommendor abides all the above-mentioned system to provide the user the needed recommendation.

For example, YouTube is the most used online video platform by all the users. YouTube provides recommendation based on the content a user has regularly surfed or about the common video which was looked upon by all the people who belong to a certain location or about a solution content which a customer read on other platforms. This combination is called the hybrid model.

Recommendation Engines are beneficial to both the producer and the customer. So, whenever your eyes catch the Product Ads which are similar to what you just explored, try to analyse which type of recommendation system it belongs to.