Research On Recommendation Methods Based On Collaborative Filtering Techniques

With the rapid development of network and information techniques , web applications can offer more and more information and services than ever before. As a result, users suffer a lot from information over-load problem. Within the huge amount web resources, recommendation system is a potential personalized technique for solving the above issues, which can adjust the content and means of services by tracking users'interests. Collaborative filtering is one of the most widely used and successful methods for recommendation, which has been made fast development in theoretical research and applications. However, collaborative filtering has got challenges, such as data sparsity, high dimensions, cold start, and real-time recommendation issues with the fast growth in the amount of users and items. In this work, by employing applied statistics methods and data mining techniques, a new similarity computation was defined and improved collaborative filtering algorithms were proposed to refine the quality. We also explored an architecture design for building E-government information recommendation system, as well as a deep discussion for the variety recommendation strategies. The main contributions of this dissertation are as follows:1. Detailed analyzed the data sparsity issue in collaborative filtering. To address the prediction inaccuracy problem caused by traditional similarity methods, we explored a new hybrid CF approach which improved the similarity coefficient computation by combining the semantic similarity of web pages with taxonomy. The missing rating values were predicted with new similarity based on the item-based CF, which alleviated sparsity of the rating matrix. Pages will be recommended based on the smoothed one. The experiment results showed the proposed algorithm can significantly improve the prediction accuracy in terms of the real web log dataset.2. Deeply researched for high dimensions issue of the rating matrix in recommendation process, and an effective local principle component analysis method was proposed. We firstly clustered the web pages based domain knowledge of the website, which caused strong inner correlation among the same class. After that, in each class several features were retained by PCA methods. Moreover, we set a threshold value to determine the user numbers for each class. This algorithm made dimensionality reduction by considering both users and items aspects. The experiments carried out on real datasets suggested that the new method can provide better recommendation quality than traditional collaborative filtering, although it required the training datasets with relatively high density.3. According to previous research results, high salability would worse the prediction quality. To address the issue, we explored a new hybrid recommendation model which combined dimensionality reduction and clustering methods. In our approach, the clusters were generated from the relatively low dimension vectors space transformed by global PCA method in first step. The online computation complexity was decreased by searching the neighbors inside one cluster instead of the whole user space. The experiments indicated that the new algorithm can produce better prediction quality and higher efficiency compared with existing algorithms, and datasets with different feature can obviously influence the recommendation results.4. We designed a public-oriented information recommendation model, which was developed to satisfy the personalized information requirements in E-government domain. The advanced recommendation architecture and main functionality modules were described as well as the key techniques needed. Due to the feature of E-government application and its service objects, various recommender strategies were adopted in our design to address the public information needs.