Userrank for item-based collaborative filtering recommendation

With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based CF approaches is that all users have the same weight when computing the item relationships. To improve the quality of recommendations, we incorporate the weight of a user, userrank, into the computation of item similarities and differentials. In this paper, a data model for userrank calculations, a PageRank-based user ranking approach, and a userrank-based item similarities/differentials computing approach are proposed. Finally, the userrank-based approaches improve the recommendation results of the typical Adjusted Cosine and Slope One item-based CF approaches.     

基于GPS软件接收机的C/A码捕获算法

为了尽量减少导航电文数据比特跳变带来的影响,在基于FFT的并行码相位搜索算法的基础上,采用两相邻毫秒数据进行捕获,再利用DFT细化频率估计,得到C/A码相位和较精确的载波频率。利用实际采集的数据进行实验,结果证实了该方法能快速有效地对C/A码进行捕获,为后续跟踪环路提供了良好的初始条件。     

一种基于位向量的位置索引压缩方法*

为了缓解大规模分布式系统中共享资源位置索引信息带来的存储压力,提出一种基于位向量的信息压缩方法。该方法建立在区/站/节点三级分层和区间对等的混合覆盖网结构上,通过在两级代理节点的有序子节点集和一定位数的位向量间建立映射关系,进而把冗余资源的位置索引信息汇聚到一条记录,逆向的位向量查询则可获取副本的分布信息。实验表明,该压缩方法达到了较高的记录压缩比,同时对提高副本定位效率起到了一定的作用。     

基于个性化情境和项目类别的资源推荐研究

传统的协同过滤推荐技术没有考虑影响用户评分的用户情境信息,但最近研究发现用户个性化情境信息直接影响着用户评分,因此在传统的协同过滤技术基础上引入用户个性化情境后推荐效果有所提高。此外可以将用户个性化情境和项目类别相结合起来。先对项目进行分类,然后再确定用户在每个项目类别下的个性化情境,同一项目类别下所有项目的用户个性化情境是相同的。在为目标项目预测评分时,先确定目标项目所在的类别,进而确定计算目标项目预测评分所用到的用户个性化情境。实验结果表明,改进后的算法较Slope one有较大提高。     

A study on a quantum-inspired evolutionary algorithm based on pair swap

A quantum-inspired evolutionary algorithm (QEA) is proposed as a stochastic algorithm to perform combinatorial optimization problems. The QEA is evolutionary computation that uses quantum bits and superposition states in quantum computation. Although the QEA is a coarse-grained parallel algorithm, it involves many parameters that must be adjusted manually. This paper proposes a new method, named pair swap, which exchanges each best solution information between two individuals instead of migration in the QEA. Experimental results show that our proposed method is a simpler algorithm and can find a high quality solution in the 0-1 knapsack problem. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.     

协作中继中断性能和差错概率分析

针对单峰窝下通信的三节点(源节点、中继节点和目的节点)协作传输模型,分析了中继节点分别采用放大转发模式和解码转发模式时对传输的中断概率和差错概率,并采用蒙特卡洛仿真对其性能进行评估。仿真结果表明,与放大转发模式相比解码转发模式可获得更低的中断概率,而解码转发模式的误比特率低于放大转发模式。     

On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. To recommend appropriate online materials for a certain learner, several characteristics of the learner, such as his/her learning style, learning modality, cognitive style and competency, need to be considered. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from past learning experiences through an ant colony optimization model. Though that results revealed the theoretical potential of the proposed method in discovering effective learning paths for learners, critical limitations arose when considering its applications in real world situations, such as the requirement of a large amount of learners and a long period of training cycles in order to discover good learning paths for learners. These practical issues motivate this research. In this paper, the aim is to resolve the aforementioned issues by devising more efficient algorithms that basically run on the same ant colony model yet requiring only a reasonable number of learners and training cycles to find satisfactory good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.     

单片机实现海明码纠错原理的研究

介绍了海明码的编码、解码、纠错原理及其构造方法,并将纠错原理应用于单片机之间的串行通信.阐述了发送机发送程序与接收机接收程序的流程,以及单片机实现海明码软件编码、解码的具体过程,并给出了找出一位差错并进行纠正的方法.该方法用于单片机遥测和遥控系统,具有高性价比、高可靠和操作简便的优点.     

基于路集矩阵与布尔运算的网络可靠度算法

分析基于路集矩阵与布尔运算的网络可靠度算法,指出其存在组合爆炸问题。为此,提出一种改进算法,引入位矢量以减少内存需求,对特殊路集进行预处理并统计全1位矢量。实验结果表明,改进算法可提高内存利用率、减少冗余运算,能在一定程度上缓解组合爆炸问题。