The Effect of Local Website Search

V. Nguyen, P. Chuan
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Abstract

Global search engine is a huge but it can’t replace local search engine. The local search engines help the end-user to retrieve easily what they want and to give accurate results than global search engines do. The end-user can also use the global search engines to augment the search performed by a local search engine, restricted to a single site. However, a single website is different from the whole web in link structure, access pattern, and possibility hit on page. To improve the performance of search sites, many researches have tried to combine original search methods with new ones such as: the PageRank, LPageRank, predictive access pattern,… This article would like to introduce a hybrid model to improve local website searched by using web server logs. This model combines capability of ranking structure link of PageRank and recommending access pattern of neural network to improve the result of search engine. It also present the way to combine search engine with PageRank, web log and neural network.To experiment with the new model, the authors have used this model for Music Machine Website with their web logs.
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本地网站搜索的效果
全球搜索引擎是一个巨大的,但它不能取代本地搜索引擎。与全局搜索引擎相比,本地搜索引擎可以帮助最终用户轻松检索他们想要的内容,并提供准确的结果。最终用户还可以使用全局搜索引擎来增强本地搜索引擎执行的搜索,而本地搜索引擎仅限于单个站点。然而,单个网站在链接结构、访问模式和页面点击率等方面都与整个网站不同。为了提高搜索站点的性能,许多研究尝试将原有的搜索方法与新的搜索方法相结合,如:PageRank、LPageRank、预测访问模式等。本文介绍了一种利用web服务器日志来改进本地网站搜索的混合模型。该模型结合了PageRank的排序结构链接能力和神经网络的推荐访问模式,提高了搜索引擎的搜索结果。提出了将搜索引擎与PageRank、网页日志和神经网络相结合的方法。为了对新模型进行实验,作者将该模型用于音乐机器网站及其网络日志。
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