Consumer Preferences And Cognition Measurement Based On Online Reviews And The Application In Personalized Recommendations

The Internet has become an important channel to collect information,which causes the "information overload",making it difficult for people to process information.The personalized recommendation system can recommend products to the user based on the user's historical behavior data.However,when recommending a specific configuration to a user,it is difficult to obtain scoring data from other users who have purchased the item.And because of the different cognition of the various attributes of the commodity,the target user will be biased in scoring the configuration of the commodity.In order to solve the above problems,the evaluation of the product configuration from the user who has purchased the product can be used in the personalized recommendation of the specific configuration.This article proposed consumer preferences and cognitive measures based on online reviews and uses them in personalized recommendations.Based on the decisionmaking theory,online reviews will be used to measure changes in perception and preference when users select product configurations.Based on the data of the online comment on the user's preference and cognition of the specific attribute configuration,the parameters of the model are corrected and dynamically adjusted by the user's interaction behavior.Recall layer co-filtering recommendations are improved,based on preference and awareness,which can recommend configuration combinations that are more consistent with their perceptions and preferences.Compared with the existing recommended algorithm,the model proposed in this paper has three main advantages.By introducing the theory of purchase decisionmaking,this paper could measure the preference and cognition.Dynamically measure user preferences and perceptions based on user implicit feedback.The collaborative filtering improved by preference and cognition can recommend configuration combinations which are more in line with user's cognitive psychology and personal preferences.The data experiment shows the method proposed in this paper perform better than the traditional recommended methods in terms of user satisfaction,accuracy and recommended convergence times.