A Proposal for Social Search System Design
A Proposal for Social Search System Design
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DOI：
10.1109/saint.2011.24
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Toyokazu Akiyama;Yukiko Kawai;Yuya Matsui;Yoshinori Kubota;T. Osaki
中科院分区：
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作者：
Toyokazu Akiyama;Yukiko Kawai;Yuya Matsui;Yoshinori Kubota;T. Osaki
我们开发了一种搜索方法，它同时使用超链接和社交链接，从而结合了搜索和社交沟通的优点。用户不仅可以快速搜索网页，还可以快速搜索当前正在访问这些网页的其他用户（“实时用户”）。搜索引擎结果页面上的每个URL都标有实时用户数。在从搜索引擎结果页面链接的每个页面上，每个超链接都标有实时用户的数量。通过使用这些链接，用户可以与其他可能对链接页面的主题有更多了解的实时用户进行通信。所有链接的页面都提供了一个窗口，用于真实的发送查询以及与其他实时用户进行通信。该窗口还显示了以前通过该页面进行通信的日志，使用户能够查看以前是否发送过类似的查询并得到答复，这将避免再次发送查询的需要。此外，用户可以突出显示链接页面上的文本，使其他用户能够快速找到重要信息，因为系统可以在访问链接页面后自动向下滚动并显示突出显示的文本。我们还开发了一个基于超链接结构和社会网络结构的页面排名算法。社交网络结构反映了实时用户的“质量”和数量。因此，用户可以真实的实时访问最流行的网页和专家。在我们的排名算法中，如何及时反映实时用户活动成为我们系统有效性的关键点。作为第一阶段，为了缩短排序方法的计算时间，我们研究了基于Open MPI和PETSc的并行计算库SLEPc。我们评估了三种计算方法，Lanzcos，Arnoldi和Krylov Schur，在SLEPC中实现。结果表明，我们可以在几十秒内完成几十万行、几十万列的概率转移矩阵的特征值和特征向量的计算。基于本文的研究，我们将在原型系统上实现我们的排序算法。
We have developed a search method that uses both hyperlinks and social links and thus combines the merits of searching and social communication. A user can quickly search for not only web pages but also for other users currently accessing those pages ("real-time users"). Each URL on the search engine results page is annotated with the number of real-time users. On each page linked from the search engine results page, each hyperlink is annotated with the number of real-time users. By using these links, a user can communicate with other real-time users who may have more knowledge about the topic of the linked page. All linked pages provide a window for sending queries in real time and for communicating with other real-time users. The window also shows a log of previous communications through that page, enabling a user to see if a similar query was previously sent and answered, which would obviate the need to send it again. Furthermore, users can highlight text on a linked page, enabling other users to quickly find important information as the system can automatically scroll down and present the highlighted text after the linked page is accessed. We have also developed a page ranking algorithm based on a hyperlink structure and a social network structure. The social network structure reflects the "quality" and number of real-time users. A user can thus access the most popular web pages and experts in real time. In our ranking algorithm, how to timely reflect real-time user activities becomes a key point for our system validity. In this paper, we especially focus on it. As a first phase, in order to shorten the calculation time of our ranking method, we investigated SLEPc, a parallel computing library based on Open MPI and PETSc. We evaluated three calculation methods, Lanzcos, Arnoldi and Krylov-Schur, implemented in SLEPC. As a result, we confirmed that we can complete the eigenvalue and the eigenvector calculation of a probability transition matrix with hundreds of thousands rows and columns in dozens of seconds. Based on the investigation of this paper, we will implement our ranking algorithm on the prototype system.