Abstract:
Within the past few years, a large variety of online stores has been started in cyberspace and people face the problems to get some things which they really want or need. Recommender systems help them to solve these problems. Thus, recommender systems are becoming popular to use in various online companies. Simultaneously, personalization becomes popular, too. By using enough knowledge about the preferences of individual customer, it is possible to provide personalized services to get more customers. This paper describes an implementation of personalized knowledge-based recommender system, recommending the most appropriate items for each customer to support an adaptive clothing company. In this paper, knowledge base and forward chaining algorithm are used as the inference engine from the company to get the best results.
