Earticle

초록

영어

A recommender system is a guide and assistance for choosing the required product or service for improving the electronic commerce systems. Most of the recommender systems use the history of customer purchase and a few are based on Semantic relatedness of purchased commodities. In this paper a semantic recommender system based on Ant Colony and Ontology dependencies is used for improvement of electronic commerce. This system comprises heuristic, stochastic, reinforcement learning in Ant Colony theory and semantic dependency in ontology characteristics. The presented system is able to recommend similar, complement and bundled products. This characteristic can overcome problems such as cold start, scalability and scarcity of information. In this paper applied tests results show the performance and efficiency of presented algorithms.

목차

Abstract
 1. Introduction
 2. Research Background
 3. Semantic Relatedness
 4. Ant Colony Optimization
 5. ANTSREC: A Semantic Recommender System based on ACS
  5.1. AntSRec Algorithm
  5.2. Adding and Removing Products
  5.3. Products Rating
  5.4. Initial Recommendation
  5.5. Recommendation Process
 6. Evaluation and Model Validation
  6.1. Test Data
  6.2. Evaluation Criteria
  6.3. Experimental Results
 7. Conclusions
 References

키워드

저자

• Mojtaba Salehi [ Department of Computer Engineering, Islamic Azad University of Khorramabad, Khorramabad, Iran ]
• Abdolhossein Fathi [ Department of Computer Engineering, Razi University, Kermanshah, Iran ]
• Fardin Abdali-Mohammadi [ Department of Computer Engineering, Razi University, Kermanshah, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605