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M. Papagelisa and D. Plexousakis, “Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents,” Engineering Applications of Artificial Intelligence, Vol. 18, No. 7, 2005, pp. 781-789. doi:10.1016/j.engappai.2005.06.010

has been cited by the following article:

• TITLE: Improving Recommender Systems in E-Commerce Using Similar Goods

  AUTHORS: Majid Khalaji, Keramat Mansouri, S. Javad Mirabedini

  KEYWORDS: Recommender System; Ontology; Similarity; Complementary; Association Rule; Collaborative Filtering

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.2, February 27, 2012

  ABSTRACT: Due to developments of information technology, most of companies and E-shops are looking for selling their products by the Web. These companies increasingly try to sell products and promote their selling strategies by personalization. In this paper, we try to design a Recommender System using association of complementary and similarity among goods and commodities and offer the best goods based on personal needs and interests. We will use ontology that can calculate the degree of complementary, the set of complementary products and the similarity, and then offer them to users. In this paper, we identify two algorithms, CSPAPT and CSPOPT. They have offered better results in comparison with the algorithm of rules; also they don’t have cool start and scalable problems in Recommender Systems.