Research On Interest-Similarity-Aware Recommendation Algorithm Based On Trust Network

The personalized recommendation system is based on the mass of information,and it provides personalized services and decision support for users through data mining,to meet the needs of different users.The foundation of personalized recommendation system is social network,which is based on the user's history,browsing information and its preferences,learning the contents of the user's interest,and recommending the information that he may be interested.This recommendation process uses dynamic collaborative group knowledge to form recommendation information in order to provide personalized service for users.On the basis of previous research,through the analysis of trust relationship between users in social networks,a new recommendation algorithm based on trust network is proposed.And this dissertation analyzed the correlation of the interest similarity and trust relationship,and introduced recommendation algorithm based on interest similarity for trust network,this dissertation puts forward three methods.Finally,four algorithms are proposed in this dissertation,and they are verified by experiments.The specific work I have done is as follows.First,read a large number of relevant literature,in-depth study of the recommendation system related research,through study and sum up previous research experience,analysis of its shortcomings,and propose new ideas.Second,a new recommendation algorithm based on trust network(NTCF algorithm)is proposed.In this paper,a new classification method of trust relation is proposed,and a new predictive scoring model is proposed.Experimental results show that compared with the traditional CF algorithm and the Ensemble Trust CF method proposed by Victor et al.,the recommendation effect is better.Third,study the interest similarity and the trust intensity.It is concluded that the interest similarity is related to the trust intensity.Fourth,based on the relationship between interest similarity and trust intensity,the interest similarity is introduced into recommendation algorithm.In this dissertation,three approaches are proposed.The first is the interest similarity as the weight prediction score model,introducing interest similarity to trust network recommendation algorithm(NITCF algorithm);the second is the interest similarity as implicit trust information added to the user trust in the collection,to supplement users trust rating matrix and sparse,so as to improve the accuracy of the recommendation system and the coverage,interest similarity as the trust network implicit trust relationship recommendation algorithm(I_TCF algorithm);the third is the interest similarity as implicit trust information added to the user trust in the collection,and then introduced to a new prediction model proposed in this dissertation,through the formation of interest similarity score data based on the new expansion of trust the network trust recommendation algorithm(I_NTCF algorithm).Experimental results show that the three algorithms are better than the CF algorithm,the ETCF algorithm and the NTCF algorithm proposed in this dissertation.