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Colonia Nicolas Bravo (Kilometro Noventa y Dos), Mexico

Gong S.,China Institute of Technology
Journal of Software | Year: 2010

Personalized recommendation systems can help people to find interesting things and they are widely used with the development of electronic commerce. Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommender systems in recent years. With the gradual increase of customers and products in electronic commerce systems, the time consuming nearest neighbor collaborative filtering search of the target customer in the total customer space resulted in the failure of ensuring the real time requirement of recommender system. At the same time, it suffers from its poor quality when the number of the records in the user database increases. Sparsity of source data set is the major reason causing the poor quality. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalized recommendation approach joins the user clustering technology and item clustering technology. Users are clustered based on users' ratings on items, and each users cluster has a cluster center. Based on the similarity between target user and cluster centers, the nearest neighbors of target user can be found and smooth the prediction where necessary. Then, the proposed approach utilizes the item clustering collaborative filtering to produce the recommendations. The recommendation joining user clustering and item clustering collaborative filtering is more scalable and more accurate than the traditional one. © 2010 ACADEMY PUBLISHER.


Wang X.-B.,Tsinghua University | Wang X.-B.,China Institute of Technology
Physical Review A - Atomic, Molecular, and Optical Physics | Year: 2013

We study the measurement-device-independent quantum key distribution (MDIQKD) in practice with limited resources when there are only three different states in implementing the decoy-state method and when there are basis-dependent coding errors. We present general formulas for the decoy-state method for two-pulse sources with three different states, which can be applied to the recently proposed MDIQKD with imperfect single-photon sources such as the coherent states or the heralded states from the parametric down-conversion. We point out that the existing result for secure MDIQKD with source coding errors does not always hold. We find that very accurate source coding is not necessary. In particular, we loosen the precision of the existing result by several orders of magnitude. © 2013 American Physical Society.


Huang L.,Wuhan University | Zhu G.,China Institute of Technology | Du X.,Temple University
IEEE Wireless Communications | Year: 2013

Femtocells have emerged as a promising solution to provide wireless broadband access coverage in cellular dead zones and indoor environments. Compared with other techniques for indoor coverage, femtocells achieve better user experience with less capital expenditure and maintenance cost. However, co-channel deployments of closed subscriber group femtocells cause coverage holes in macrocells due to co-channel interference. To address this problem, cognitive radio technology has been integrated with femtocells. CR-enabled femtocells can actively sense their environment and exploit the network side information obtained from sensing to adaptively mitigate interference. We investigate three CRenabled interference mitigation techniques, including opportunistic interference avoidance, interference cancellation, and interference alignment. Macrocell activities can be obtained without significant overhead in femtocells. In this article, we present a joint opportunistic interference avoidance scheme with Gale-Shapley spectrum sharing (GSOIA) based on the interweave paradigm to mitigate both tier interferences in macro/femto heterogeneous networks. In this scheme, cognitive femtocells opportunistically communicate over available spectrum with minimal interference to macrocells; different femtocells are assigned orthogonal spectrum resources with a one-to-one matching policy to avoid intratier interference. Our simulations show considerable performance improvement of the GSOIA scheme and validate the potential benefits of CRenabled femtocells for in-home coverage. © 2002-2012 IEEE.


Gong S.,China Institute of Technology
Journal of Computers | Year: 2011

With the rapid development of the Internet and the wide application of e-commerce, recommender system has become a necessity and collaborative filtering is the most successful technology for building recommendation systems. There are many problems in the recommendation approaches, such as data sparsity problem, the issue of new items and scalability issues. Item-based collaborative filtering algorithms can improve the scalability and the traditional user-based collaborative filtering methods, to avoid the bottlenecks of computing users' correlations by considering the relationships among items. But it still worked poor in solving the issues of sparsity, predictions for new items. In order to effectively solve several problems, this paper presented a recommendation algorithm on integration of item semantic similarity and item rating similarity. The item semantic similarity is calculated combining Earth Mover's Distance and Proportional Transportation Distance, which can utilize the semantic information to measure the similarity between two items based on a solution to the transportation problem from linear optimization1. Then producing recommendation used item-based collaborative filtering integrating the semantic similarity and rating similarity. The presented approach can effectively alleviate the sparsity problem in e-commerce recommender systems. © 2011 ACADEMY PUBLISHER.


Gong S.,China Institute of Technology
International Journal of Digital Content Technology and its Applications | Year: 2012

With the emergence and evolution of Networks, the information on the Internet has increased greatly. Retrieving useful information from a large amount of information has become a key technology in the information area. The application of personalized recommendation in the Internet effectively improved its service, especially the service of E-commerce. Traditional search engine do not take different user's interest into consideration, so the result they retrieved cannot satisfy user's specified needs. In order to effectively solve the problem, this paper presented a personalized recommendation system employing user interest model for content-based filtering. This paper analyzes the system of five different components: document information extraction, document vectors representation, user interest model representation; matching algorithms, user feedback update. This personalized recommendation system can describe user's interest type and interest degree well, and can enhance the personalized information service efficiency.

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