Multi-Criteria Recommendation Systems to Foster Online Grocery
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 da...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/11/3747 |
| _version_ | 1797532295393968128 |
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| author | Manar Mohamed Hafez Rebeca P. Díaz Redondo Ana Fernández Vilas Héctor Olivera Pazó |
| author_facet | Manar Mohamed Hafez Rebeca P. Díaz Redondo Ana Fernández Vilas Héctor Olivera Pazó |
| author_sort | Manar Mohamed Hafez |
| collection | DOAJ |
| description | 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 challenge when recommending products is 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 shopping, which depends on product characteristics such as composition, packaging, nutrition table, allergen, and so forth. For our evaluation, we conducted a user and expert survey. Finally, we 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. |
| first_indexed | 2024-03-10T10:57:06Z |
| format | Article |
| id | doaj.art-f3c3d924ee774bd8953b7e4a893d0084 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:57:06Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f3c3d924ee774bd8953b7e4a893d00842023-11-21T21:48:55ZengMDPI AGSensors1424-82202021-05-012111374710.3390/s21113747Multi-Criteria Recommendation Systems to Foster Online GroceryManar Mohamed Hafez0Rebeca P. Díaz Redondo1Ana Fernández Vilas2Héctor Olivera Pazó3College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT)-Smart Village, Giza P.O. Box 12676, EgyptAtlanTTic, Information & Computing Lab, Universidade de Vigo, 36310 Vigo, SpainAtlanTTic, Information & Computing Lab, Universidade de Vigo, 36310 Vigo, SpainAtlanTTic, Information & Computing Lab, Universidade de Vigo, 36310 Vigo, SpainWith 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 challenge when recommending products is 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 shopping, which depends on product characteristics such as composition, packaging, nutrition table, allergen, and so forth. For our evaluation, we conducted a user and expert survey. Finally, we 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.https://www.mdpi.com/1424-8220/21/11/3747recommender systemsretail marketdigital transformationgrocery industrybag-of-wordDoc2Vec |
| spellingShingle | Manar Mohamed Hafez Rebeca P. Díaz Redondo Ana Fernández Vilas Héctor Olivera Pazó Multi-Criteria Recommendation Systems to Foster Online Grocery Sensors recommender systems retail market digital transformation grocery industry bag-of-word Doc2Vec |
| title | Multi-Criteria Recommendation Systems to Foster Online Grocery |
| title_full | Multi-Criteria Recommendation Systems to Foster Online Grocery |
| title_fullStr | Multi-Criteria Recommendation Systems to Foster Online Grocery |
| title_full_unstemmed | Multi-Criteria Recommendation Systems to Foster Online Grocery |
| title_short | Multi-Criteria Recommendation Systems to Foster Online Grocery |
| title_sort | multi criteria recommendation systems to foster online grocery |
| topic | recommender systems retail market digital transformation grocery industry bag-of-word Doc2Vec |
| url | https://www.mdpi.com/1424-8220/21/11/3747 |
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