Resource Collaborative Filtering Recommendation Based On Items And Computational Pragmatics

The volume of information over the Internet is increasing at a tremendous rate. Users are usually confronted with situations in which they have been exposed with too many options to choose from. They need help to explore and to filter out irrelevant information based on their preferences. The corresponding requirement is to describe and capture relevant information for directing users based on specific interests and needs. The supporting systems are identified as personalized recommendation systems. The recommendation systems will also act as a stimulant to further E-Commerce sales growth by converting browsers into buyers, preventing user losing, increasing clicks-ratio, and building customer loyalty. Recently recommendation systems have gradually become an important part in Web Intelligence technologies. Personalized recommendation approaches have gained great momentum both in the commercial and research areas.Recent years, collaborative filtering recommendation technique has gain rapid growth in theory and practice, but along with the scale of the application systems further expanding, it is facing a series of new challenges. At same time, pragmatics theory applied to information systems has also gradually become a research hotspot. This dissertation is focused on the application of the basic ideas of computational pragmatics in recommendation systems. The main research includes: the personalized context analysis, the model for user belief rank computing, recommendation methodologies and algorithms based on context and belief. The research is to solve the context-free, independence-assumption, and shilling-attacks problem in collaborative filtering recommendation approaches from a novel perspective, and to improve the quality of the recommendations. Meanwhile the application of computational pragmatics to personalized recommendation domain will also improve the development of the pragmatics. The main research and contributions in this dissertation are summarized as follows:①The current state-of-the-art methods and techniques of personalized recommendation systems are reviewed. In the dissertation, recommendation techniques are summarized, their characteristics and the scope of application are analyzed. On that basis, the research on collaborative filtering algorithms is summarized and classified, and the problem of the research was analyzed. ②The development of pragmatics and the relationships and similarities between pragmatics and personalized recommendation are analyzed. The relationships between the three key concepts of computational pragmatics and personalized recommendation are further analyzed. The research has set up the theoretical foundation for the research on pragmatics-based personalized recommendation.③Several approaches are proposed to solve the context-free problem by incorporating personalized contextual information into collaborative filtering recommendation systems. Firstly, a collaborative filtering recommendation algorithm based on the most significant personalized contextual parameter is proposed. The experimental results show that our approach is helpful to improve the precision of rating prediction. Secondly, a collaborative filtering recommendation approach based on personalized multi-contextual parameter is proposed. In this approach, a rating prediction model is learned by BP and RBF neural networks. Then the model is used to extrapolate unknown ratings and make recommendations. An experiment is given to evaluate the approach. The experimental results show that the approach is more helpful to improve the precision of rating prediction. The RBF neural network is more helpful than BP is.④A user belief rank based collaborative filtering recommendation approach is proposed. The approach provides a novel idea and method for the independence-assumption problem. Firstly, the definition and characteristics of belief, and the relationships between belief and recommendation is analyzed. Secondly, on that basis, a PageRank-based UserRank (belief rank) algorithm is proposed. Thirdly, and two collaborative filtering recommendation algorithms are proposed based on UserRank and Adjusted Cosine, and UserRank and Slope One. Finally, the experiments show that our approaches provide better recommendation results than Adjusted Cosine and Slope One. At the end, as trial research for the recommendation effectiveness based on the combination of contextual information and belief information, a comprehensive recommendation algorithm is proposed.⑤A pragmatics-based learning resource personalized recommendation system is proposed. In the system, the analysis approach for personalized context, the computation method for user rank, and the personalized context based and user rank based recommendation approaches are applied. The architecture, functional modules, and system interfaces are provided.