  With the exponential increase in information, it has become imperative to
design mechanisms that allow users to access what matters to them as quickly as
possible. The recommendation system ($RS$) with information technology
development is the solution, it is an intelligent system. Various types of data
can be collected on items of interest to users and presented as
recommendations. $RS$ also play a very important role in e-commerce. The
purpose of recommending a product is to designate the most appropriate
designation for a specific product. The major challenges when recommending
products are insufficient information about the products and the categories to
which they belong. In this paper, we transform the product data using two
methods of document representation: bag-of-words (BOW) and the neural
network-based document combination known as vector-based (Doc2Vec). We propose
three-criteria recommendation systems (product, package, and health) for each
document representation method to foster online grocery, which depends on
product characteristics such as (composition, packaging, nutrition table,
allergen, etc.). For our evaluation, we conducted a user and expert survey.
Finally, we have compared the performance of these three criteria for each
document representation method, discovering that the neural network-based
(Doc2Vec) performs better and completely alters the results.
