Blog > How AI help with Product Recommendation
Why Should Businesses Use AI for Product Recommendation?
AI-based recommendation systems are great applications for internet-based companies, especially E-commerce and streaming businesses. The significant benefit for e-commerce companies is that all the necessary user data can be captured when users visit an e-commerce business website. AI helps to get powerful analytics that can be useful to grow the business.
1. Improve Customer Satisfaction
The key benefit of using AI is to improve customer satisfaction as the system can provide the user with more meaningful content, whether these are products, songs, or videos.
2. Provide Personalization
The personalization levels increase massively because an e-commerce website built a customer profile. The website owner gets the opportunity to learn from their customer’s data and provide them with a personalized user experience.
3. Improve Product Discovery
Because of personalization, product discovery will improve as users might be finding products they usually wouldn’t.
Recommendation Engines
The way recommendation engines work is as follows three main types such as:
1. Collaborative Filtering
It can predict the behaviour of users based on the similarity they have with other users. In the example of Netflix, the system can recommend movies without understanding what the movie is about.
Collaborative Filtering Using Machine Learning Cycle
The machine learning cycle for collaborative filtering consists of the following steps such as:
Data Source: Understand the data source and business with all machine learning projects. In this case, the generic user behaviour serves all users and the activities and preferences they have.
Data Preparation: In the second step, the user has to select the data, clean it, and transform it into the algorithm.
Algorithm Application: There are two key algorithms for collaborative filtering:
user-user collaborative filtering searching for lookalike customers and offering products based on what the lookalike has chosen. And the second main algorithm is item-item collaborative filtering, in which, rather than finding a lookalike customer, it finds products that are lookalike.
Algorithm Optimization: In the fourth stage, we will compare the algorithms, the impact, and the results are measured, and we can do that by measuring the increased revenue or the increased watching time. This cycle is then repeated until the results are to an acceptable standard. As already mentioned earlier, the coded-based filtering approach is based on item features and user profile data. This data can range from age, demographic power, sales history, click rates, etc. And the item features can be based on specified contents or labels of the item, for example, natural language processing.
We used to understand the underlying content, and once the raw data is prepared, we can apply many different algorithms to this application. One is cluster analysis which groups data objects based on information that describes the object and their relationships. The second is the neural network that can train to protect ratings or interactions based on the item and user attributes.
The user can also use deep neural nets to predict the next action based on historical actions and contents.
2. Content-Based Filtering
This is a more complex approach that consists of two main factors, the user profile and the items such as a product. It tries to recommend products which have similar to the ones that a user has liked in the past; the way it worked is that the system builds a profile of the user based on, for example, their search history click behaviour interests and tries to find similar features of an item such as a movie or a product. This requires a system to understand the content of the item. For example, when a user watches an action movie with certain actors’ ratings and other features, the system can recommend movies that have similar content that those the user has already watched; the hybrid recommendation system is a combination of the first two where it combines the outcomes of each and puts them together using certain scoring criteria.
3. Hybrid Recommendations
The hybrid engine combines the input from both mentioned systems to provide recommendations; these are often complex mathematical calculations that take various criteria from each engine to combine to achieve the highest quality recommendation engine.
Author: SVCIT Editorial
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