Research And Implementation Of Multi-Agent Cooperative E-Commerce Recommendation Model

With the development of economic globalization and the rapid rise of the Internet,diversification and rapid transaction mode of e-commerce is gradually coming into sight,which does not only bring us great convenience,but also to promote world economic development.On the other hand,the rapid expansion of the scale of e-commerce also makes users spend a lot of time browsing unrelated goods;for sellers,what they wish urgently is to recommend products to the users in the most appropriate way.This leads to the emergence of e-commerce recommender system.However,the existing recommender systems have a series of problems such as lack of terminal self-adaption and outstanding personalized capacity,and content-based algorithm or collaborative filtering algorithm is widely used in the current e-commerce recommender systems,in which cold-start problem and scalability exists.In order to further improve the accuracy and efficiency of product recommendation in e-commerce recommender system,a multi-agent recommendation model was proposed.First of all,this paper reviews current related works of recommender system and Multi-Agent at home and abroad,and comparative analysis the mainstream recommendation algorithm and recommendation model.Secondly,a Multi-Agent cooperative e-commerce recommendation model was designed.The model is based on Multi-Agent technique in Artificial Intelligence,which adopted terminal self-adaption by considering the importance of mobile terminal in the mobile Internet era.Furthermore,this paper adopted both content-based algorithm and collaborative filtering algorithm to the model.According to developed recommendation algorithm and model,the recommendation accuracy and speed has been improved.Finally,the paper verifies the performance of the algorithm and the model by simulation experiments,which include the comparison of average recommendation accuracy and comparison of average waiting time.The experimental results show that by adopting the recommendation model,the recommendation accuracy can be improved and the average waiting time can be reduced.Meanwhile,the model has a good reference value on the fields similar to e-commerce.