Research on recommender system based on ontology and genetic algorithm
Allied to the extensive use of online shopping, product recommendation on websites is vitally important for E-commerce. Consequently, collaborative filtering and content-based filtering have been widely used in recommendation systems on E-commerce websites. However, these filtering methods have many problems, such as cold start, prejudiced ratings and inaccurate suggestions. To generate valid and accurate suggestions, researchers have proposed integrating semantic features of the data in an ontology into the recommendation process. However, most existing studies only include the type and features of a product without considering the relational characteristics thereof. Given that the relational characteristics can provide much useful information during the recommendation process, we have designed an easily realizable recommendation system framework based on relational data by integrating the relational data in the domain ontology and applying a genetic algorithm to process the recommendation. Experimental results show that there are obvious improvements in the methods for dealing with sparsity and cold start problems as well as the accuracy and timeliness of recommendations.
recommendation ontology genetic algorithm semantic web associated data