AI in Personalized Product recommendation
A personalized product recommendation isn’t based on an assumption or guess. Personalized recommendations are based on user behavior. These are items that have been frequently viewed, considered, or purchased with the one the customer is currently considering.
A product recommendation is basically a filtering system that seeks to predict and show the items that a user would like to purchase. It may not be entirely accurate, but if it shows you what you like then it is doing its job right.
Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. Mostly used in the digital domain, majority of today’s E-Commerce sites like eBay, Amazon, Alibaba etc make use of their proprietary recommendation algorithms in order to better serve the customers with the products they are bound to like.
Personalized Product Recommendation Tips and Stats
Product recommendations can multiply profits.
Unfortunately, many eCommerce companies install a simple plugin and leave it at that. The truth is not all recommendations are the same.
To be successful, you need sophisticated product recommendation engines that are able to make sense of shopper’s web behavior.
Creating a predictive, retail product recommendations system
Step 1: Collect data to base personal recommendations on
Step 2: Use AI to determine which algorithm to use based on user’s context
Step 3: Overriding machine learning in select cases (merchandising rules)
