Wine recommendation algorithm based on partitioning and stacking integration strategy for Chinese wine consumers

Abstract

This study tries to propose a wine recommendation algorithm based on partitioning and Stacking Integration Strategy for Chinese wine consumers. The approaches follow the idea of partitioning, decomposing traditional recommendation task into several subtasks according to wine attributes, using neural network, support vector machine (SVM), decision tree, random forest, optimized random forest, Adaboost and XGBoost as recommendation models. Then, based on Stacking integration method, five models are screened out for each recommendation index as the base classifier, and the decision tree or logistic regression model is selected as the meta-learner to construct a two-layer Stacking integration framework. Finally, the optimal recommendation algorithm be built for recommendation subtasks according to the prediction accuracy. The result showed that the Stacking integrated recommendation model was suitable for the recommendation of eight attributes including colour, sweetness, foamability, mouthfeel, aroma type, year, packaging and brand, while SVM model was suitable to recommend aroma concentration and price, and the XGboost model was most appropriate for origin. This study would subserve consumers to choose the wine more easily and conveniently and provide support for wine companies to improve customer satisfaction with consumer services. The study expands the approach of concerning research and proposes a specific multi-model recommendation strategy based on artificial intelligence models to recommend multiattribute commodities.

User value identification based on an improved consumer value segmentation algorithm

“…In terms of the number of clusters N , since most fields subdivide a large market into 2–6 sub-markets in practice (Bruwer et al. , 2017; Mu et al. , 2017; Balci and Cetin, 2020; Cristina and Jean-Pierre, 2020; Huang et al.…”

“…parameters. In terms of the number of clusters N, since most fields subdivide a large market into 2-6 sub-markets in practice (Bruwer et al, 2017;Mu et al, 2017;Balci and Cetin, 2020;Cristina and Jean-Pierre, 2020;Huang et al, 2020), this study successively discusses the segmentation results of each algorithm when the number of sub-markets is 2-6. In this work, the SC and CH obtained are labeled as W_SC and W_CH when considering the consumer value, and vice versa as UW_SC and UW_CH.…”

Incorporation of Plants Extracts and Essential Oils into Starch‐Based Films: A Comprehensive Review

“…[ 2 ] Considering the global environmental waste issues associated with conventional and synthetic plastics, the only solution for replacing petroleum‐based plastics is the development of green packaging. [ 3 ] These packaging materials are based on biodegradable polymers of natural origin, such as polysaccharides, lipids, and proteins. [ 4,5 ]…”

“…[2] Considering the global environmental waste issues associated with conventional and synthetic plastics, the only solution for replacing petroleum-based plastics is the development of green packaging. [3] These packaging materials are based on biodegradable polymers of natural origin, such as polysaccharides, lipids, and proteins. [4,5] Starch is a promising biopolymer among the polymers that presents a more proper scope in developing green packaging films because of its abundant availability, renewability, reduced cost, biodegradability, and better film-forming properties (Farooq et al [6] ; Zhang et al [7] ; Noonim et al, 2021).…”

Chinese consumers’ attribute preference prediction for table grapes

“…Chu et al (2020) used multivariate logistic regression to predict consumer preferences for wine attributes. Mu et al (2022) also used logistic regression model for recommending consumers' wine attributes. In conclusion, logistic regression has an important significance for predicting consumer preferences regarding attributes of table grapes.…”