为了给IT界的业务开发者提供一套获取移动定位能力的简单接口,借鉴Parlay X API的理念提出了基于Web Services的定位接入网关,设计了接入网关的内部结构和外部接口,实现了一个基于MLP协议的移动定位接入网关。测试结果表明该接入网关能...为了给IT界的业务开发者提供一套获取移动定位能力的简单接口,借鉴Parlay X API的理念提出了基于Web Services的定位接入网关,设计了接入网关的内部结构和外部接口,实现了一个基于MLP协议的移动定位接入网关。测试结果表明该接入网关能很好地工作,并具有良好的性能。展开更多
Collaborative filtering （CF） has been widely applied to recommender systems, since it can assist users to discover their favorite items. Similarity measurement that measures the similarity between two users or items...Collaborative filtering （CF） has been widely applied to recommender systems, since it can assist users to discover their favorite items. Similarity measurement that measures the similarity between two users or items is critical to CF. However, traditional similarity measurement approaches for memory-based CF can be strongly improved. In this paper, we propose a novel similarity measurement, named Jaccard Uniform Operator Distance （JacUOD）, to effectively measure the similarity. Our JacUOD approach aims at unifying similarity comparison for vectors in different multidimensional vector spaces. Compared with traditional similarity measurement approaches, JacUOD properly handles dimension-number difference for different vector spaces. We conduct experiments based on the well-known MovieLens datasets, and take user-based CF as an example to show the effectiveness of our approach. The experimental results show that our JacUOD approach achieves better prediction accuracy than traditional similarity measurement approaches.展开更多
基金supported by the National Basic Research 973 Program of China under Grant No.2011CB302506the National Natural Science Foundation of China under Grant Nos.61001118,61132001,61003067+1 种基金the National Major Science and Technology Project of New Generation Broadband Wireless Network of China under Grant No.2010ZX03004-001the Fundamental Research Funds for the Central Universities of Beijing University of Posts and Telecommunications of China under Grant No.2011RC0502
文摘Collaborative filtering （CF） has been widely applied to recommender systems, since it can assist users to discover their favorite items. Similarity measurement that measures the similarity between two users or items is critical to CF. However, traditional similarity measurement approaches for memory-based CF can be strongly improved. In this paper, we propose a novel similarity measurement, named Jaccard Uniform Operator Distance （JacUOD）, to effectively measure the similarity. Our JacUOD approach aims at unifying similarity comparison for vectors in different multidimensional vector spaces. Compared with traditional similarity measurement approaches, JacUOD properly handles dimension-number difference for different vector spaces. We conduct experiments based on the well-known MovieLens datasets, and take user-based CF as an example to show the effectiveness of our approach. The experimental results show that our JacUOD approach achieves better prediction accuracy than traditional similarity measurement approaches.
