Research On Interest Cross Fusion Recommendation Method:Based On Interpretability Of Knowledge Graph Paths

With the development of internet technology and explosive data growth,recommendation has gradually become increasingly important in fields such as e-commerce platforms,music websites,and social networking sites.The early recommendation methods are mainly content-based and collaborative filtering algorithms.However,in actual recommendation scenarios,the traditional content-based and collaborative filtering recommendation algorithms have some problems,such as cold start and failure to adapt to data sparse scenarios.In order to solve these problems,recommendation methods based on knowledge graphs have gradually become a research hotspot in recent years.Knowledge graph is a structured representation of entity,relationship,attribute and other information.It can integrate knowledge from different fields and provide more comprehensive and rich information sources for recommendation.However,the existing knowledge graph-based recommendation methods also have the problem of indiscriminate use of user project paths,conveying unclear information and having a negative impact on interpretability.In response to these issues,this article proposes an interest cross fusion recommendation method based on the interpretability of knowledge graph paths,abbreviated as ICFR(Interest Cross Fusion Recommendation).It also uses knowledge graph as a source of auxiliary information,utilizing the structural information and path relationship information of knowledge graph to enhance recommendation.ICFR adopts a deep end-to-end framework and combines embedding tasks with fusion recommendation tasks to form a multi task feature learning method.The interest cross compression unit plays a connecting role in achieving automatic sharing of potential features,while learning high-order interactions between user paths and similar path patterns in the knowledge graph,proving that path level intersections can share more potential features.The main research content of this article is as follows:Firstly,in order to better express the user’s interest features,this article uses a feature expression method based on the knowledge graph interest element path.Specifically,first extract the corresponding meta path from the generated user path.Then,LSTM is used to model the path to obtain semantic feature representations on the path.Next,use a bidirectional BFS path generation algorithm based on relationship search to search for paths with the same node type and relationship pattern.Finally,the semantic feature expression of the obtained path is used to enhance the expressibility of user interest feature preferences.Secondly,a new model called ICFR,namely the interest cross fusion recommendation method,was proposed to fuse users’ interest preferences.This method achieves high-order interaction between paths by extracting path features with the same relationship pattern in user paths and knowledge graphs,and intersecting the two.Compared to the simple intersection of items and entities,the intersection of paths can generate more feature interactions,thereby generating more auxiliary information and providing interpretability for recommendations.Finally,the algorithm model proposed in this article is compared with representative algorithm models,and comparative experiments are conducted in two recommendation scenarios: click through rate(CTR)prediction and top-k recommendation.The values of AUC,ACC,Precision@K and Recall@K of each algorithm model are obtained.The results show that the algorithm performance of the proposed model is superior to other comparison algorithm models in terms of movie,book and music recommendation.Finally,by comparing the AUC values in the click through rate prediction scenario after reducing the proportion of the training set in the movie dataset,the performance of each algorithm model in sparse data scenarios was tested.The results showed that the algorithm model proposed in this paper still maintained good performance even in situations where user project interaction was sparse.