Predicting preferences and likes with Machine Learning Recommendation Engine

The internet is more powerful than ever before. The online shopping sites that hold items on our carts conveniently suggests things we may have missed or intend to add before checking out. The video-sharing site keeps us engage for hours by showing content we can’t seem to get enough of and knows exactly how to keep us entertained.
These web services are powered by recommendation engine, an information filtering system composed of machine learning algorithms that predicts preferences or suggest content and products which may be attractive or relevant to a particular user. These are evident in Amazon’s product recommendation, Spotify’s daily mix suggestions and Netflix movie recommendations.

Recommender systems sits at the core of marketing analytics, allowing organizations to navigate through a sea of content to create personalized content. Recommendation engine software brings together and match user and content data. In essence, anything can be suggested to a user, from clothes, music, applications, movies, and even people based on interest, physical attributes and location, such as in dating apps.

Recommendation engine system are designed to:

3 Types of Recommendation Engine Systems and Recommendation Engine Algorithm

Collaborative Recommendation is a simple way to make relevant suggestions to customers, understand which products are preferred by the users and to what degree in comparison with the other products. It is based on user interaction history within the platform. This is the recommender system commonly used by eCommerce sites and marketplaces.

Content-based Recommender System focuses on content-recommendation built around a specific item or inventory (content, product, service). If a user is looking for a specific item, it recommends “similar” items based on specification, model, purpose.

Hybrid Model uses both transaction data and metadata to provide suggestions. It combines both content-based and collaborative models. An example is Netflix, which recommends movies based on user interest and movie features.

Benefits of Using Recommendation Engines

Content Discovery

It is highly important for customers to find what they are looking for and discover something they may have an interest in every time they visit a business site. Content discovery is in fact one of the main reasons why recommendation system algorithm is relevant now. Providing personalized suggestions to users enabling them to consume content and get more out of the experience.

User Engagement

Providing a steady stream of relevant content increases user engagement by maintaining user interest. If a service or product delivers what the user wants it will continue to use and engage that service. Making users regularly visit the website and use the app for content update.

Dynamic Audience Insights

Companies utilizing recommendation engine software get valuable audience and user insights, important in improving operation and providing better products and services according to their needs and demands, inspiring innovation.

Use Cases

eCommerce and online stores

Product recommendation is at the core of eCommerce. The purchase sequence can be triggered by a simple relevant product suggestion to a customer.

Content Aggregation

Content aggregation platforms use recommendation systems to push relevant content such as movies, music, and videos. By providing suggested content users stay on-site longer and encourage further activity.

Social Media

Creating connections is the essence of social media sites and with a recommendation engine, these sites provide consistent user engagement and a “people you might know” section to help users grow their network.

Recruitment and Human Resource Sites

Professional-use-oriented platforms such as LinkedIn use a recommendation engine that is highly important for HR professionals looking for suitable candidates for available positions. The recommender system provides suggestions based on relevant attributes.

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