Research And Application Of Knowledge Graph Based On Clothing And Apparel

With the progress and development of society,clothing gradually becomes a way to express personality and show oneself.In the traditional apparel recommendation system,the importance of potential and implicit correlations of factors such as color and style of apparel far exceeds the explicit relationships between apparel.However,the clothing recommendation method based only on relational database modeling and storage lacks the connection between clothing data and data,and suffers from two major problems of data sparsity and cold start.Therefore,it is very meaningful to explore and study the personalized recommendation method for clothing and apparel to solve the data sparsity and cold start problems while enhancing the hit rate of recommendations.Knowledge graph,as an effective auxiliary class of information in hybrid recommendation,has received a lot of attention from scholars in recent years.The essence of knowledge graph is a structured network,which contains rich information of data nodes and attributes.At present,a part of expertise graphs have been constructed in academia and industry,but the construction and application of knowledge graphs in the apparel field and recommendation are still in the initial exploration stage.Therefore,it is also of theoretical and practical significance to integrate knowledge graph into the field of personalized recommendation of apparel.As shown above,in order to solve the problems of sparse data and cold start of traditional recommendation systems in the clothing and apparel domain,and to enhance the accuracy of recommendations and improve the comprehensibility of recommendation methods.The main work of this paper is as follows.(1)A knowledge graph in the clothing and apparel domain is constructed.This paper takes apparel matching as the entry point,mines the data contents in expert matching recommendation,product category and user history behavior data from the perspective of knowledge association,aligns and disambiguates the entity attributes by knowledge fusion technology,constructs the knowledge graph of apparel domain and generates OWL data by using protégé tool.(2)A recommendation method based on the knowledge graph of clothing and apparel is proposed.In this paper,we use Apriori association rule algorithm and TF-IDF algorithm to mine the information of association rules implied in the data table from the association relationship between product categories and the semantic similarity of apparel labels.The ontology inference model framework of the knowledge graph is constructed by Jena inference machine,and the obtained implicit association relations are added into the knowledge graph as custom rules to complement the data.(3)A visual query recommendation system for clothing and apparel knowledge graph is built.The Django framework was chosen to develop the backend of the system to realize the interaction between the front and backend of the visual query system.Through the constructed clothing and apparel knowledge graph,Spar QL statements are used for product query and rule inference is performed in Jena reasoning machine to get the final recommendation query results.(4)Empirical research analysis.In order to verify the reliability and accuracy of the personalized recommendation method based on the clothing and apparel knowledge graph in this paper,the algorithm is compared with the collaborative filtering algorithm based on users and products in this paper.Finally,the effect of clothing and apparel recommendation is evaluated by accuracy and recall rate.Through experiments,it is proved that the algorithm proposed in this paper has better effect on the recommendation performance and effect improvement...