SigRA: A New Similarity Computation Method in Recommendation System

2017 ◽  
Author(s):  
Xiaokun Wu ◽  
Yongfeng Huang
Keyword(s):  
Recommendation System ◽  
Computation Method ◽  
Similarity Computation

Research on Modeling Technology of Virtual Resources Cloud Pool for Group Enterprises Based on Ontology

2013 ◽  
Vol 347-350 ◽  
pp. 3287-3291
Author(s):  
Yun Xia Wang ◽  
Zhi Liang Wang ◽  
Cheng Chong Gao
Keyword(s):  
Cloud Manufacturing ◽  
Computation Method ◽  
Case Based Reasoning ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Modeling Technology ◽  
Manufacturing Resources ◽  
Similarity Computation ◽  
Virtual Resources ◽  
Group Enterprises

To realize cloud manufacturing (CMfg) production in group enterprises, manufacturing resources and modeling technologies of cloud pool were studied. According to the characteristics of group enterprises, manufacturing resources were analyzed and classified into human, equipment, materials, cooperation resources and so on. Then, the realization method which manufacturing resources mapped into virtual resources was researched, and a layer platform for cloud manufacturing was proposed. Taking CNC machine tool as an example, the ontology model was built with Semantic Web and OWL based on ontology theory. Finally, using semantic similarity computation method and case-based reasoning, the virtual resources were intelligent searched and matched so that manufacturing resources can realize unification, sharing and reuse.

A Semantic Information Content Based Method for Evaluating FCA Concept Similarity

2018 ◽  
Vol 12 (2) ◽  
pp. 77-93
Author(s):  
Hongtao Huang ◽  
Cunliang Liang ◽  
Haizhi Ye
Keyword(s):  
Information Content ◽  
Semantic Information ◽  
Computation Method ◽  
Occurrence Probability ◽  
Lower Accuracy ◽  
Weighted Bipartite Graph ◽  
Similarity Computation ◽  
Content Similarity ◽  
Probability Information ◽  
Concept Similarity

Probability information content-based FCA concepts similarity computation method relies on the frequency of concepts in corpus, it takes only the occurrence probability as information content metric to compute FCA concept similarity, which leads to lower accuracy. This article introduces a semantic information content-based method for FCA concept similarity evaluation, in addition to the occurrence probability, it takes the superordinate and subordinate semantic relationship of concepts to measure information content, which makes the generic and specific degree of concepts more accurate. Then the semantic information content similarity can be calculated with the help of an ISA hierarchy which is derived from the domain ontology. The difference between this method and probability information content is that the evaluation of semantic information content is independent of corpus. Furthermore, semantic information content can be used for FCA concept similarity evaluation, and the weighted bipartite graph is also utilized to help improve the efficiency of the similarity evaluation. The experimental results show that this semantic information content based FCA concept similarity computation method improves the accuracy of probabilistic information content based method effectively without loss of time performance.

Similarity Computation Method for Collaborative Filtering Based on Optimization

2010 ◽  
Vol 14 (6) ◽  
pp. 654-660
Author(s):  
Akihiro Yamashita ◽  
◽  
Hidenori Kawamura ◽  
Keiji Suzuki
Keyword(s):  
Collaborative Filtering ◽  
Correlation Coefficient ◽  
Optimal Solution ◽  
Computation Method ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Active User ◽  
Pearson's Correlation ◽  
Similarity Computation ◽  
Pearson's Correlation Coefficient

The recommender system provides personalized recommendations at many e-commerce websites. Collaborative filtering is one of the most popular and effective recommendation algorithms. User-based collaborative filtering, the conventional approach in collaborative filtering, uses user similarity computed based on user item rating. Recommendations are provided by calculating rating predictions based on similarity. Pearson’s correlation coefficient or cosign distance is used as similarity. Until now, a lot of discussions for efficient similarity computation were given by many researchers. Despite active discussion of similarity computation, little computation has been made for optimal similarity in collaborative filtering. In this research, similarity optimization problem was formulated by defining similarities between an active user and other users as a vector variable. The quasi-optimal solution was obtained based on Particle Swarm Optimization (PSO) approach, compared to Pearson’s correlation coefficient. Experimental results based on agentbased simulation and sample dataset show that similarity based on PSO improves recommendation accuracy. We also found that PSO-based similarity computation provides rating predictions for unknown ratings more accurately than conventional similarity computation.

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