Read through the white paper. Had to be more focused in reading. Uniqe project. Users can minimize their energy spending less time in searching the products they wish to purchase by utilizing artificial intelligence. It advises the users on choosing the products to buy by taking into account item components, quality, price, shipping time and costs as well as other factors.
Wish the project gets succeded. Would like to question here. What type of products you advice on? All types of products? How do you differentiate and screen out the difference between products? Kindly apologise if these questions seem silly.
Thank you Greenkarki for the feedback.
Now to answer your question... In the beginning we will start with so called wish-lists (a list of product usually bought together). We will leverage the machine deep learning on our collected data to present the most precise results possible. We already have working prototype for AI categorization and brand recognition. This prototype is currently in alpha stage. After we have confirmed the correct methodology for product discovery, we will increase the range of products up to the point when we have most of the product categories covered.
For screening out the difference between products we will use different techniques, all depending on the category type. As it is always interesting to go deep into more complex examples, I can tell you that in some cases we will really dissect the products specification up to the point of spare parts the product uses. When we know the spare parts of the product we can compare them and consequentially determine the differences between products.
Hopefully this answers your question.
Thank you for the support,
Ziga
