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.
Highlights
Viral foods refer to foods that are widely welcomed by the public for a period of time through online marketing and publicity
The types of viral foods have been increasing, and an increasing number of young people are keen to try a variety of viral foods, which have become a major component of young people’s consumption
Sharif et al [8] used matrix factorization and implemented singular value decomposition (SVD) to develop a recommendation approach that is superior to other algorithms of collaborative filtering in dealing with the sparsity data problem
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With the rapid development, safety, standards, quality, and other issues have become the concern of regulatory authorities and public opinions (Galanakis et al [3]) It is the common desire of consumers to quickly and accurately find viral foods that meet their own tastes among a wide range of online platform recommendations, and save unnecessary search time and waste of funds. Personalized recommendation is required by major e-commerce platforms, which use an appropriate recommendation approach to connect personalized foods to users with different preferences. These approaches mainly rely on the user’s historical behavior information. The ingenious application of the personalized recommendation approach to the food recommendation system is beneficial in improving the accuracy of the recommendation
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