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:
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.
Recommendation Engines Examples
Facebook — “People You May Know”
Netflix — “Other Movies You May Enjoy”
LinkedIn — “Jobs You May Be Interested In”
Amazon — “Customer who bought this item also bought ….”
Google — “Visually Similar Images”
YouTube — “Recommended Videos”
Waze — “Best Route”
Types of Recommendation Engines
Three types of Recommendation Engines are operational.
Content Filtering: This method doesn’t require other user’s data while recommending a product to a particular user. For instance, a company can recommend a specific item to an individual based on their interest in a comedy or Action movie. The main drawback of Content filtering is that if a user has purchased only kitchen products online, it can recommend only kitchen items, not any other products.
Collaborative Filtering: It uses data of other users before recommending a product to an individual. For instance, it gathers all the common attributes and interests of different people and maps them to the products they buy. Further, based on the matching of individual traits, it recommends similar products. In simple it analyzes user behavior, activities, and preferences before recommending a product.
Hybrid Filtering: This model uses the metadata of Collaborative models and transactional data of Content models to recommend a product. Generally, this model outperforms the first two models.
A Typical example of Recommendation on E-commerce sites
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.
How does a Recommendation Engine Work?
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:
Data Gathering: In this step, the recommendation engine gathers both implicit and explicit data from various sources.
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.
Data Storage: The data collected from various sources must be stored in storage mediums for efficient retrieval in the following steps
Data Analysis: Different analysis techniques are applied to analyze the gathered data. Primary data analysis methods include Near-Real-time analysis, Batch analysis, and real-time analysis.
Data Filtering: As mentioned earlier, it is the final step used during recommendations. Content filtering, Collaborative filtering, and hybrid filtering are used to develop apt recommendations for the customers.
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