Xiaojun Guo

Solving the Sparsity Problem in Recommender Systems Using Association Retrieval

Recommender systems are being widely applied in many fields, such as e-commerce etc, to provide products, services and information to potential customers. Collaborative filtering as the most successful approach, which recommends contents to the current customers mainly is based on the past transactions and feedback of the similar customer. However, it is difficult to distinguish the similar interests between customers because the sparsity problem is caused by the insufficient number of the transactions and feedback data, which confined the usability of the collaborative filtering. This paper proposed the direct similarity and the indirect similarity between users, and computed the similarity matrix through the relative distance between the user’s rating; using association retrieval technology to explore the transitive associations based on the user’s feedback data, realized a new collaborative filtering approach to alleviate the sparsity problem and improved the quality of the recommendation. In the end, we implemented experiment based on Movielens data set, the experiment results indicated that the proposed approach can effectively alleviate the sparsity problem, have good coverage rate and recommendation quality.

Semantic Learning Service Personalized

Abstract To provide users with more suitable and personalized service, personalization is widely used in various fields. Current e-Learning systems search for learning resources using information search technology, based on the keywords that selected or inputted by the user. Due to lack of semantic analysis for keywords and exploring the user contexts, the system cannot provide a good learning experiment. In this paper, we defined the concept and characteristic of the personalized learning service, and proposed a semantic learning service personalized framework. Moreover, we made full use of semantic technology, using ontologies to represent the learning contents and user profile, mining and utilizing the friendship and membership of the social relationship to construct the user social relationship profile, and improved the collaboration filtering algorithm to recommend personalized learning resources for users. The results of the empirical evaluation show that the approach is effectiveness in augmenting ...

Ant colony optimization for PON network design

Optical access technology has quickly got a worldwide development. Currently, the deployment of equipment and fiber lines in the optical access network require complex manual design. However, because of non-standard artificial design, it is difficult to make the network reasonable, and increasing post-engineering and maintenance difficulties. Based on the partition algorithm in the previous article, this paper presents a new partitioning algorithm for the PON network design, which is a combination of the Ant Colony Optimization algorithm and partitioning algorithm. Compared with other algorithms, the ant colony optimization is more efficient and more accurate. It is very suitable for solving calculating the capacity of the large and complex network. Therefore, we use ant colony optimization for PON network design. By entering the number of nodes, users, service traffic, the unit cost parameters, it can automatically calculate the number and configuration of OLT, which can help to optimize the network layout, and save the network construction costs.

Personalized learning service framework based on Semantic Web Technologies

Personalization is widely used in various fields to provide users with more suitable and personalized service. In this paper, a personalized learning service framework based on Semantic Web Technologies is proposed. The framework made full use of ontology to construct the semantic metadata model and user profile, mining and utilizing the friendship and membership to provide personalized learning services that include learning service and learning support service by a new ontology-based collaborative recommendation algorithm. Experiments in this paper showed that the proposed architecture is effective.

Partitioning Algorithm for PON Network Design

Optical access technology has quickly got a worldwide development. Currently, the deployment of equipment and fiber lines in the optical access network need professionals to design. But, because the lack of a unified standard, it is difficult to make the network reasonable. By analyzing the PON architecture model, this article talks about an improved partitioning algorithm for PON network design. By entering some parameters, it can automatically calculate the number and configuration of OLT, which can help to optimize the network layout, and save the network construction costs.

Open Rainbow Services-Oriented Testbed: Low Cost Cloud Computing Platform for Open Systems Research

This paper describes Open Rainbow, a low cost cloud computing testbed with the characteristic of protection of private data through localization of data and global sign-on, compares and analysis the cost of different cloud computing platforms. It demonstrates the utilities of Rainbow by the experiments of data processing time of rainbow, comparison between rainbow and grid, global-scale performance of rainbow, and then brings forward measures of optimizing the services of rainbow. The experiments with the real data provided by the practical project, the regional-economy-based modeling and optimization of collaborative logistics system in manufacturing enterprise, shows that Rainbow can address the requirements of the researchers in the developing countries that are eager to narrow the digital divide in cloud computing.

Semantic Web-Based Learning Resources Organization

Massive Learning resources are heterogeneous disorder, and the computers cannot handle message lack of semantic, the introduction of the Semantic Web Architecture for human cognition knowledge of the law related areas of knowledge points. This paper presents a knowledge-point of teaching resources as the core organizations overall architecture, the semantic Web technology described by the semantic object metadata and ontology database to establish the appropriate knowledge, learning object library, the formation of knowledge points, between learning objects and learning resources mapping for the associated knowledge points provide support for semantic level. Semantic Web architecture based on massive network of learning resources describing the organization, dynamic aggregation, knowledge modeling of innovative, knowledge management, semantic knowledge management and service network for solving scientific problems have a greater theoretical significance, with a wide range of applications .

Q&A System Based on Personalized Service

It tremendously impacts the degree of satisfaction of users whether E-learning system can promptly and effectively solve the user’s problems. In e-Learning, the users become thinkers from audience. To a puzzling question, they often seek help through the search, consulting friends or posting on BBS. However these methods guarantee neither progress of learning nor validity of the selected way. In this paper, Q&A System Based on Personalized Service (QASBPS) is proposed. The service based on semantic web technology, using semantic information extraction and multi-document summarization technology to generate answers. Otherwise, the most direct and effective way is to consult the peoples, who have mastered the knowledge point about explaining and illation. So the system use the user’s social relationship map and interest map to recommend helpers.