Research On Electronic Commerence Recommendation Technology Based On Domain Ontology

With the popularization of the Internet, E-Commerce systems bring more and more information for businesses and customers, and it becomes much more difficult for consumers to find services they want in a timely manner from the massive online sources. To address this issue, a variety of recommendation systems were proposed and great attentions have been paid on this new technology, which has become a hotspot in recent researches.Although the recommendation systems in E-Commerce have been very successful in both research and practice, challenging problems still remain. Aimed at solving the main challenges of recommendation systems in E-Commerce, this dissertation attempts to integrate domain ontology with Web usage mining for recommendation personalization and gives a rewarding research on recommendation algorithms and related models in E-Commerce recommendation systems in order to improve the accuracy and the instantaneity of the systems. The main research work and innovative points discussed in this dissertation are as follows:(1) Based on analyzing different means of domain ontology construction, a new method is proposed to construct domain ontology for personalized recommendation.(2) Sparsity is one of the most important issues in collaborative filtering recommendation. To deal with the sparisty of user-item rating, a new collaborative filtering recommendation algorithm which is based on domain ontology and interest drift has been developed.In the new algorithm, the semantic similarity between types and values can be computed according to the domain ontology, the predicting rating of items unfilled by users can be predicted with the semantic similarity and filled according to the K Nearest Neighbors(KNN), then the recommendations can be made using the filled user-item rating matrix. Besides, the user's features can be used for user clustering to reduce the selection scope of nearest neighbors. Taking the changes in users' preferences into account, a forgetfulness function f(t) is introduced to adjust the importance of rating considering the rating time. The experiment results show that the new algorithm can solve the sparse problem effectively and has better recommendation quality than the traditional algorithm.(3) Without using the semantics of objects, the traditional Web usage mining in personalized recommendation does not consider relative semantic knowledge, so the accuracy of recommendation is low. In order to solve this problem, a personalized recommendation model which integrating domain ontology with Web usage mining for personalization is presented. An innovative personalized recommendation algorithm based on semantic clustering is devised in this model. An ontology-based vector space model is setting up after the preprocessing on Web usage data with domain ontology. The transaction data are clustered with K-Means Agglomerative Nesting algorithm. The cancroids of clusters can be used to generate user preference and recommendation data sets.(4) A new multiple recommendation prototype system is designed and implemented. The new system can support different multiple recommendation models such as non-personalized recommendation, personalized recommendation for registered users and personalized recommendation for unregistered users. By mining on the Web usage data and user item-rating data, analyzing the user attribute information and user rating records, the model learns the potential interests of the users, and provides instant and accurate personalized recommendation services. The effectiveness of the model is verified through the development of a personalized recommendation prototype system based on a film domain ontology.