A Hybrid Recommendation Approach for Viral Food Based on Online Reviews.
Foods
; 10(8)2021 Aug 04.
Article
in English
| MEDLINE | ID: covidwho-1376772
ABSTRACT
Nowadays, there are many types of viral foods and consumers expect to be able to quickly find foods that meet their own tastes. Traditional recommendation systems make recommendations based on the popularity of viral foods or user ratings. However, because of the different sentimental levels of users, deviations occur and it is difficult to meet the user's specific needs. Based on the characteristics of viral food, this paper constructs a hybrid recommendation approach based on viral food reviews and label attribute data. A user-based recommendation approach is combined with a content-based recommendation approach in a weighted combination. Compared with the traditional recommendation approaches, it is found that the hybrid recommendation approach performs more accurately in identifying the sentiments of user evaluations, and takes into account the similarities between users and foods. We can conclude that the proposed hybrid recommendation approach combined with the sentimental value of food reviews provides novel insights into improving the existing recommendation system used by e-commerce platforms.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
Language:
English
Year:
2021
Document Type:
Article
Affiliation country:
Foods10081801
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