Recommendation engines drive nearly 35% of Amazon’s revenue and 75% of what people watch on Netflix. Recommendation engines are software that analyzes available data to make suggestions for something that an online user might be interested in.
A variety of technologies and techniques are needed to filter large amounts of data and provide a smaller, focused body of suggestions for the user. Metadata tagging on videos in conjunction with data about user behavior is used by Netflix to come up with recommended movies and TV shows for specific members. The semi-structured member data, including locations, job titles, skill sets and industries, fuels the “Jobs you might be interested in” section of LinkedIn.
Alletec can help you build Recommendation engines leveraging both – Content based, and Collaborative filtering methods. In a world where every customer is increasingly faced with multiple choices, if you can recommend a few items based on customer’s needs and interests, it will create a positive impact on the user experience and lead to frequent visits. Winning businesses leverage machine learning technologies to help customers choose. Data is filtered using different algorithms and a recommendation engine recommends the most relevant items to users. It first captures the past behavior of a customer (if available) and based on that, recommends products which the users might be likely to buy. If the past behavior data is not available, suggestions can be made on basis of best-selling, or what the business wants to promote.
Building a Recommendation Engine is a multi-step and evolving process, involving:
• Data Collection: Both explicit and implicit data collection mechanisms are needed. Customer’s search history, clicks and order history are important data sources.
• Data Storage: SQL, no-SQL, and object storage mechanisms could be used, depending on the type of data. More the data, better the recommendation.
• Data Filtering: Content based, or Collaborative – this involves extracting the relevant information required to make the final recommendations.
