A Personalized Recommendation Approach Based On Linguistic Concept Lattice With Fuzzy Object

The current recommendation system mainly focus on evaluating the similarity of users or items through the user's historical behaviors,and then recommends corresponding items for the user.Although this type of method is highly accurate,however,it cannot provide exploratory recommendations to users in the process of cognition,and it also faces some problems such as cold boot and recommendation interpretation vague.Therefore,this paper proposes a personalized recommendation approach based on linguistic concept lattice with fuzzy object by combining association rules and formal concept analysis.The main research results of this paper are as follows:1?Formal concept analysis has been widely studied as an important tool for data processing and knowledge discovery.Aiming at the problem that the classical formal context can not handle linguistic value,the linguistic concept formal context is proposed to construct the linguistic concept lattice.The linguistic concept formal context puts forward to constructing linguistic concept lattice.With an attempt of knowledge reduction,the multi-granularity similarity relationship between linguistic concepts is defined on the basis of granular computing which further divides the linguistic concept set into three parts under l-granularity(i.e.,core linguistic concept,unnecessary linguistic concept,and relative necessary linguistic concept).Based on the discernibility matrix,it reflects the differences between fuzzy objects,and proposes a linguistic concept reduction algorithm with incomplete linguistic concept formal context,which is used to solve the problem of data sparsity in recommendation system.2?Aiming at the problem of missing information in the linguistic concept formal context,a new algorithm to complete the incomplete linguistic concept formal context based on the closeness degree between fuzzy objects is proposed.Based on the Boolean matrix and Boolean factor analysis method,the linguistic concept knowledge reduction algorithm to extract the core linguistic concept and reduce the scale of linguistic concept lattice is proposed to handle the complexity,which is achieved by computing the similarity of linguistic concept knowledge in order to handle different types of linguistic information and concept knowledge.3?This algorithm describes the relationship between the object and the linguistic concept based on the linguistic concept lattice with fuzzy object,and shows the generalization and instantiation relationship between the concepts.The formed Hasse diagram realizes the visualization of fuzzy data,which effectively solves the shortcomings of recommended interpretation ambiguity in the personalized recommendation system.In addition,it also considers the cognitive process of each user,and overcomes the cold boot problem in the personalized recommendation system.It introduces linguistic values into association rules,which can better reflect human thinking habits.4?By collecting behavior data between users and objects through a questionnaire,a personalized recommendation approach based on linguistic concept lattice with fuzzy object is designed and implemented,which illustrates the correctness and effectiveness of the recommendation approach.By numerical analysis,the advantages of the proposed methods are compared.