Implementation And Application Of Recommender System Based On Learning Network Representation

The current society has entered the period of information overload.E-commerce platforms,User Generated Content communities,online education platforms,and other platforms produce massive amounts of content data every day.Interactive behaviors between users and contents also produce a large amount of data.Therefore,how to help users filter a large amount of information and help users quickly find contents or products that users are most likely to be interested in is the key to improving user experience.Recommender system as an information filtering system can combine the user's feedback information on items to personally recommend items that may be of interest to the user.However,with more and more users and items,the amount of data is getting larger and the data is getting sparse,recommender system is currently faced with the challenges of declining recommendation quality and lower recommendation real-time performance caused by sparse data and massive data.In order to improve the recommendation quality of the recommender system and enhance the user experience,this paper focuses on the design of the recommendation algorithm and system architecture to improve the recommendation quality and real-time performance of the current recommender system under data sparse and massive data scenario.First of all,this paper will introduce the background,significance and research status of the recommender system.Secondly,introduce the theory and system development techniques related to the work of this paper.Based on this,we study recommendation algorithm based on Skip-Gram of natural language processing model,design an LN-N2V-TW-CF algorithm based on Transform-Embedding-Recommender workflow,verify and test the recommendation effect on online education and movie dataset.Finally,we combine with big data technology and front-end system development technology,design a real-time recommender framework based on offline model and online recommendation,implement a complete real-time recommender system application based on Apache Spark and Django framework.The main results of this paper are as follows:(1)In terms of recommendation quality,under the framework of Transform-Embedding-Recommender,do research on recommendation algorithm based on natural language processing model Skip-Gram;We design the LN-N2V-TW-CF recommendation algorithm based on learning networks data transformation,Node2 Vec embedding and time weight item-based collaborative filtering.Among them,the data transform method of the learning network can better capture the topological relationship between items;Node2Vec can better represent and capture the relationship between nodes in the learning network;The time-weighted and item-based collaborative filtering algorithm introduces the impact of time changes on user interest.Compared with some existing collaborative filtering recommendation algorithms,the proposed algorithm improves the recommendation quality of recommender system in the education recommendation scenario and movie recommendation scenario.(2)In terms of real-time recommendation,in order to ensure the real-time and recommendation timeliness of the recommender system,this paper designs a real-time recommender system architecture by combining offline model and online recommendation.In order to accelerate the calculation of the network representation part,this work deploys the network representation part on the Apache Spark distributed computing platform,and combines the Django framework to implement the Restful style recommender system backend,which realizes the combination of offline model update and online real-time recommendation.Finally,we demonstrate the interaction between the recommender system and the user by the front-end interface,verify the feasibility of the algorithm,and realize a MOOC recommender system application.