User-to-User Matching Services through Prediction of Mutual Satisfaction Based on Deep Neural Network
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With the development of the sharing economy, existing recommender services are changing from user–itemrecommendations to user–user recommendations. The most important consideration is that all users shouldhave the best possible satisfaction. To achieve this outcome, the matching service adds information betweenusers and items necessary for the existing recommender service and information between users, so higher-leveldata mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employingthe prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, andthe properties considered for recommendations were set by filtering and weighting. Based on this process, weimplemented a convolutional neural network (CNN)–deep neural network (DNN)-based model that can predicteach supplier’s satisfaction from the consumer perspective and each consumer’s satisfaction from the supplierperspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendationlist is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data,which is a representative sharing economy platform. The proposed model is meaningful in that it has beenoptimized for the sharing economy and recommendations that reflect user-specific priorities.
Abstract
With the development of the sharing economy, existing recommender services are changing from user–itemrecommendations to user–user recommendations. The most important consideration is that all users shouldhave the best possible satisfaction. To achieve this outcome, the matching service adds information betweenusers and items necessary for the existing recommender service and information between users, so higher-leveldata mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employingthe prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, andthe properties considered for recommendations were set by filtering and weighting. Based on this process, weimplemented a convolutional neural network (CNN)–deep neural network (DNN)-based model that can predicteach supplier’s satisfaction from the consumer perspective and each consumer’s satisfaction from the supplierperspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendationlist is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data,which is a representative sharing economy platform. The proposed model is meaningful in that it has beenoptimized for the sharing economy and recommendations that reflect user-specific priorities.
참고문헌 (25)
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