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Multi-criteria recommendation systems to foster online grocery
Information Retrieval · Submitted 12 Dec 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | The recommendation system ($RS$) with information technology development is the solution, it is an intelligent system. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | Various types of data can be collected on items of interest to users and presented as recommendations. | background 0.99 | 0.08 | 0.02 | asserted 0.88 |
| s3 | $RS$ also play a very important role in e-commerce. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s4 | The purpose of recommending a product is to designate the most appropriate designation for a specific product. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s5 | The major challenges when recommending products are insufficient information about the products and the categories to which they belong. | gap 0.54 | 0.06 | 0.02 | asserted 1.00 |
| s6 | 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). | method 0.98 | 0.95 | 0.03 | asserted 0.97 |
| s7 | 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.). | contribution 0.83 | 0.88 | 0.09 | asserted 0.98 |
| s8 | For our evaluation, we conducted a user and expert survey. | method 0.98 | 0.96 | 0.02 | asserted 0.98 |
| s9 | 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. | result 1.00 | 0.96 | 0.83 | asserted 1.00 |
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The paper
Original abstract
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.
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What it cites · 2 sentences without a confident label
Cites as background 2 papers
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A new idea was recently proposed inspired by the neural network language model, and the model proposed is known as Word to Vector (word2vec) [ref] .
Efficient Estimation of Word Representations in Vector Space · Representation Models · 0.85
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Two models generate the representation of word2vec have been presented in order to produce such dense word embeddings: the Continuous Bag of Word (CBOW) model [ref] and the Skip-Gram model [ref] .
Combining Language and Vision with a Multimodal Skip-gram Model · Representation Models · 0.88
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