Bidirectional Service Recommendation Based On POI Sensitive Knowledge Graph

With the rapid development of the Internet industry,services have become more diverse and customer needs have become more complex.How to recommend services that satisfy their individual needs has become a hot issue in the field of service computing.Although traditional service recommendation methods such as collaborative filtering algorithms have been widely used,they often lack details and potentials of service content.The analysis of semantic relevance,so there is still much room for improvement in terms of its accuracy.At the same time,the large increase in the number of services makes the service competition more intense,so the service provider,the service provider,needs to continuously improve the services it provides.In view of the above research background,this paper takes customer reviews as the research entry point,because the customer reviews contain a large amount of information that can express the customer's specific interest preferences.In this topic,this information is called the user interest focus POI,that is,the characteristics of the service that the customer pays attention to during the delivery process.This paper firstly mines the POI in the customer review data,and integrates the multi-faceted information into the knowledge graph structure to generate the POI sensitive knowledge graph,and uses the knowledge graph to provide service recommendation for the customer and provide improved recommendations on service content for service providers.The specific research content includes the following four parts:(1)User interest focus POI mining and refining process: This paper uses an unsupervised machine learning algorithm to mine the user interest focus POI in the customer review data,and then,emotional analysis technology is used to analyze customer's specific emotional tendencies toward POI to depict customer portraits in fine granularity.Finally,synonyms of POI with high semantic similarity are normalized.(2)Construction and representation learning of POI sensitive knowledge graph:Integrate POI of user interest into the construction of knowledge graph,and design the structure of knowledge graph according to the order of entity extraction,relationship extraction and attribute extraction.The POI perception knowledge graph is generated,and at the end,the knowledge representation of the knowledge graph is learned,which lays a foundation for the follow-up work.(3)Service recommendation for customer's individualized needs: With the help of POI perception knowledge graph,a recommendation algorithm based on knowledge graph representation learning is proposed for the customer side in supply and demand relationship,and then the optimal parameter setting of the algorithm is found through experiments.And a variety of commonly used recommendation algorithms are designed as comparisons to verify the superiority and feasibility of the algorithm.(4)Recommendations for service improvement of providers: With the help of POI perception knowledge graph,a novel service content recommendation algorithm for improvement is proposed for service providers in supply and demand.Firstly,the concepts of service competition diagram and degree of alert are put forward.Based on this,the recommendation algorithm for service providers to recommend service content to be improved is designed.Finally,relevant experiments are carried out to analyze the performance of recommendation algorithm and give case study.Based on POI mining of customer interest concerns in customer review data,this paper constructs a POI perception knowledge graph including customer information,service provider information,POI information and their relationships.On the basis of POI perception knowledge graph,this paper designs a recommendation algorithm for customers to recommend service providers who may be interested in it and alos designs a recommendation algorithm for service providers to provide improved recommendations on service content for them.