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Artificial Intelligence has helped many sectors to move towards technology in many ways. For example, it has helped in the development of self-driving vehicles, weather forecasting, image recognition software, E-commerce website, etc. Artificial Intelligence has played a vital role in making fiction a reality. Recommendation Engines are a product of AI that has taken the arena of advertising in e-commerce with rigor. Let us dive deep into the world of AI recommendation engines and advertising:
|TABLE OF CONTENT
|1. What are Recommendation Engines?
|2. Recommendation Engines Examples
|3. Types of Recommendation Engines
|4. A Typical example of Recommendation on E-commerce sites
|5. How does a Recommendation Engine Work?
|6. How CloudThat Can Help You in Mastering AI & Data Analytics?
A recommendation engine is a tool for data filtering that employs machine learning algorithms to recommend the most relevant items to the customer. This works on the principle of finding patterns in consumer behavior data.
Before the advent of e-commerce and the proliferation of AI and the Internet into every household, recommendations used to happen through salespersons, friends, relatives through word of mouth. Now this task is taken over by algorithms and machines.
Three types of Recommendation Engines are operational.
Amazon Search Engine: If a user searches for a product, it shows the relevant product to the user also, it recommends to the user to buy few products which are related to a previously purchased product.
In the screenshot above. I am buying a Samsung galaxy M02s device on amazon; they also suggested I purchase related products by showcasing the device back case and protection glass on Amazon, which reminds the user also to buy these products, which increases their sales. In addition, several suggestions pop up. Machine Learning which is a sub-domain of AI makes this possible. Therefore, whenever you search on the E-commerce website, you’ll quickly get relevant results concerning the search term used. Thus, allowing the E-commerce website to increase sales.
Recommendation Engines are synonyms for Living beings. As Living beings grow by taking in food, Recommendation Engines grow by consuming data. Also, as living beings learn through interaction with the environment, Recommendation engines learn through Machine Learning algorithms.
Four steps are involved in the workflow of a recommendation engine:
Examples of Implicit data are search history, clicks, cart events, search log, order history data, etc.
Explicit data is collected based on customer input like product reviews and ratings on online platforms.
As a pioneer in the Cloud Computing training realm, we are a Microsoft Gold Partner, AWS (Amazon Web Services) Advanced Consulting Partner, and Training partner. Also, as we are Google Cloud Partners delivering best-in-industry training for Azure, AWS, and GCP (Google Cloud Platform). We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within cloud space. Our certification training course Exam DP-100: Design & Implement Data Science Solution on Azure and Exam DA-100: Analyzing Data with Microsoft Power BI helps you trained in Machine Learning and Data Analytics on Cloud platforms and thus reap the benefits to start building recommendation engines for your organizations.
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