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.
Highlights
Today, the world wide web is a great place of digitaldocuments considering the developments of information technology
The main column of this table is as following: Id: Code of every product based on UNSPSC standard Name: Name of every product Is-a: High class of every product Need: Needed property of a product Two popular measurement criteria used in Recommender Systems, called “precition” and “recall”, have been invoked in this paper to evaluate the designed system
We presented a Recommender System based on complementary and similarity products
Summary
The world wide web is a great place of digitaldocuments considering the developments of information technology. Researches have been done that led to the developments of new methods for Recommender Systems. The success of a website depends on customers and visitors attractions and definition of user’sneeds is necessary for improving applications of a website. Recommender Systems try to predict interests and needs of users using data collections and offer a list of user’s needs. These Recommender Systems have many problems and led to some problems in great websites about offering goods and commodities to users. The relationships of goods have important function in designing Recommender Systems. The most important relationships can be named complementary association and similarity
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