Fusion Tags And Rate Of Tea Products Personalized Recommendation Research

With the rapid development of Internet, China, as an agricultural country, meets growing network trade. Personalized recommendation, one of the central issues in the field of e-commerce, will be the inevitable trend in the development of e-commerce of agricultural products. Agricultural products are usually recognized as common products in the recommendation of existing e-commerce platform, but their characteristics are always overlooked. Moreover, recommendation of traditional agricultural products only pays attention to single scoring factor or labeling factor. Its accuracy is not high which cannot meets the needs of consumers.This topic is to study tea products, which are typical in agricultural products and scoring factor is added to labeling factor. On the basis of consisting model of tea products users,similarity is calculated in the positive and negative module respectively in face of sparsity of background data and imbalance of marked distribution. Finally, recommended list is produced based on project recommendation of Collaborative filtering method.The main contents of this study include the following aspects:Firstly, this study proposes a user model of combining labeling and scoring, whose advantages are:this model not only considers one part but combines users'labeling and scoring. It integrates labeling sets of positive and negative evaluation products by use of users'scoring and calculates similarity in positive and negative modules and labeling weight marked by users. Recommended list based on project recommendation of Collaborative filtering eases data sparsity to some extent.Secondly, the above recommendation system model is tested and evaluated. On the basis of recommendation model of agricultural products combining labeling and scoring, model is tested by prediction accuracy and ranking scoring making use of real data sets. Experiment shows that this recommended result is more accurate than result from single scoring or label.Thirdly, recommendation system of tea products combining labeling and scoring realizes more accurate recommendation of tea products.This research result has certain reference significance for promoting development of future agricultural e-commerce.