Multi-criteria recommendation systems to foster online grocery
This work addresses recommendation challenges for online grocery shopping, but it is incremental as it applies existing document representation methods to a specific domain.
The paper tackled the problem of insufficient product information in online grocery recommendation systems by proposing multi-criteria systems based on product, package, and health criteria, using bag-of-words and Doc2Vec document representations, and found that Doc2Vec performed better in evaluations.
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.