Study on the key technology of personalized recommendation of case-based reasoning

2012 ◽  
Author(s):  
Fuliang Li ◽  
Yanfeng Bai ◽  
Jieli Sun
Keyword(s):  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Key Technology ◽  
Case Based

Study on the Key Technology of Personalized Recommendation of Case-Based Reasoning

2013 ◽  
Vol 380-384 ◽  
pp. 2271-2275
Author(s):  
Fu Liang Li ◽  
Yan Feng Bai ◽  
Jie Li Sun
Keyword(s):  
Recommendation System ◽  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Key Technology ◽  
Case Based

The key technology of personalized recommendation based on CBR involves the representation and the organization of case, construction and maintenance of multiple cases library, judging of the similarity of case and methods of retrieval, and the combination of personalized recommendation technology. The four interrelated aspects are the important links to design the personalized recommendation system. This paper studies the key technology of the personalized recommendation system based on CBR.

Research on Multiple Cases Database Construction of Case-Based Reasoning Personalized Recommendation System

2013 ◽  
Vol 303-306 ◽  
pp. 1448-1451
Author(s):  
Jie Li Sun ◽  
Yun Lu ◽  
Fu Liang Li
Keyword(s):  
Recommendation System ◽  
Construction Method ◽  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Case Method ◽  
Database Construction ◽  
Case Based

The multiple cases database construction is one of the important links to design the personalized recommendation system. Personalized recommendation system case can be organized with multiple cases database based on expert experience and thinking patterns, combined with the traditional case method of organization. This paper studies the multiple cases database construction method of the personalized recommendation system based-CBR.

Research on Realization Mechanism of Case-Based Reasoning System Facing the Industrial Design

2011 ◽  
Vol 328-330 ◽  
pp. 738-742
Author(s):  
Wen Zhang
Keyword(s):  
Design Process ◽  
Industrial Design ◽  
Case Based Reasoning ◽  
Key Technology ◽  
System Building ◽  
Reasoning System ◽  
Computer Aided ◽  
Problems And Solutions ◽  
Function Module ◽  
Case Based

The research in mainly conducted on the realization mechanism of CAS (Computer Aided System) facing the Industrial Design, though analysis on the discipline characteristics of Industrial Design as well as the design process based on the PCM method, the advantages as well as feasibility of case-based reasoning system as a kind of system building pattern is discussed. Based on the combination of the discipline characteristics of Industrial Design and the theory of case-based reasoning, the research on the comprehensive realization mechanism is conducted on aspects in the form of systematic function module planning, major problems and solutions involved in all links as well as key technology discussions, providing reference for the establishment of the case-based reasoning system together with the construction of similar auxiliary systems facing the Industrial Design.

A Particle Swarm Optimization Approach to Fuzzy Case-based Reasoning in the Framework of Collaborative Filtering

2014 ◽  
Vol 1 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Shweta Tyagi ◽  
Kamal K. Bharadwaj
Keyword(s):  
Particle Swarm Optimization ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Case Based

The particle Swarm Optimization (PSO) algorithm, as one of the most effective search algorithm inspired from nature, is successfully applied in a variety of fields and is demonstrating fairly immense potential for development. Recently, researchers are investigating the use of PSO algorithm in the realm of personalized recommendation systems for providing tailored suggestions to users. Collaborative filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. However, data sparsity and prediction accuracy are the major concerns related to CF techniques. In order to handle these problems, this paper proposes a novel approach to CF technique by employing fuzzy case-based reasoning (FCBR) augmented with PSO algorithm, called PSO/FCBR/CF technique. In this method, the PSO algorithm is utilized to estimate the features importance and assign their weights accordingly in the process of fuzzy case-based reasoning (FCBR) for the computation of similarity between users and items. In this way, PSO embedded FCBR algorithm is applied for the prediction of missing values in user-item rating matrix and then CF technique is employed to generate recommendations for an active user. The experimental results clearly reveal that the proposed scheme, PSO/FCBR/CF, deals with the problem of sparsity as well as improves the prediction accuracy when compared with other state of the art CF schemes.

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