The Research And Application Of Personalized Recommendation Based On Tag

Traditional recommendation methods usually do not have the comprehensive treatment for query result,not only does not take into account the user 's individualized demand,is also not do the detail study on the characteristic o f resources,so the retrieval is poor and user satisfaction is low.At the same time,the birds of a feather flock together,although everyone has his own interests,but making the users with same interests into group,is of great significance to improve the performance of personalized recommendation.To solve above problems,this topic combined with group recommendation,proposes a personalized recommendation method based on user profile.At first,this paper puts forward the improvement of TF-IDF(inverse document frequency)method,the fusion processing is conducted with the number of tags which the user had used and the total number of resources which the user had marked while constructing user profile,the tag weights of active users has relatively reduced,also,the fusion processing is conducted with the number of tags which had labeled the resource and the number of users who had annotated the resource,so that the profile could more accurately reflect the characteristics of users and resources.Second,this paper had further studied the traditional cosine similarit y calculation method,and had put forward improvement strategy to improve the accuracy of the retrieved result as a whole,that is combining with the number of tags which has matched.Then,on the basis of the traditional recommendation system,calculated the correlation of the preliminary result and the computed user profile,had applied the user interest model into search effectively.The effectiveness of the proposed method is verified by some related experiments.Finally,used the method proposed in this paper to experiment on poverty alleviation system repeatedly,and had collected users into groups using cluste ring technology according to the similarity of user 's interest model,had computed the user group interest to updating user's interest model,combined personalized recommendation and group recommendation perfectly,and make the recommendation result more convincing.