Google 网页排名背后的技术

 

搜索排名的核心技术源自已有50年历史的学术课题 Information Retrieval （IR），IR 技术使用统计学原理对文字的使用频率等属性进行研究并对结果进行排名。建立在 IR 理论上的 Google 搜索同时借助链接，网页结构等等技术形成独特的搜索技术。

理解网页多年来在网络爬虫与索引系统上投入巨资，因此，Google 拥有非常庞大并且是最新的网页索引，除此之外，Google 还使用一些最新技术提高索引质量，比如，他们开发了 一种技术，可以在字面意思之外理解一个网页所表达的重要概念，人们使用意大利语言搜索 "galleria sprovieri londra"，会找到伦敦的 Sprovieri Gallery，尽管 Sprovieri Gallery 主页上既没有 London，也没有 Londra 字样。在美国，人们搜索 "cool tech pc vancouver, wa"，会找到 www.cooltechpc.com，然而 www.cooltechpc.com 的主页上没有任何文字表明他们位于 Vancouver。其它技术包括，区分一个网页中的重要或非重要文字，以及网页内容的新鲜度。

理解语义可以通过用户提供的几个搜索关 键词，理解用户的真实意图。他们在拼写纠正，词义，以及概念分析方面处于非常领先的位置。很多人都或多或少体验过 Google 的拼写纠正功能，比如搜索 "kofee annan"，Google 会问你搜索的是否 "kofi annan"，然而，当有人搜索 "kofee beans"，Google 会纠正成 "coffee beans"。（Google 事实上已经在尝试语义技术 - 译者）词义是 Google 尝试理解查询语义的基础，也是 Google 遇到的最大难题。一些在人看来显而易见的东西，机器却很难自动处理。用户并不想对使用什么词汇进行查询而费神，人们甚至压根不知道该使用什么进行查询。在 这种时候，Google 的词义系统便可以发挥作用，词义系统可以对查询语句进行非常复杂的修正，比如，查询 "Dr Zhivago" 的时候，Google 知道 Dr 代表 Doctor ，而查询 "Rodeo Dr" 的时候，Dr 代表 Drive。用户搜索 "back bumper repair" 的时候，结果是 rear bumper repair，而搜索 "Ramstein ab"，Google 能够将
ab 理解成 Air Base，"b&b ab" 会理解为 Alberta 的 Bed and Breakfasts 。Google 将这种词义理解系统发展到上百种不同语言。

在搜索排名中使用的另一项技术是概念识别，该技术可以对查询的内容进行概念识别，比如，我们 查询 "new york times square church"，Google
知道我们实际上查询的是纽约时代广场上的那座著名教堂，而不是纽约时报中的某篇文章。概念识别技术并不止这些，Google 还对其进行加强以正确地识别语义，比如，搜索 "PC and its impact on people"，事实上是搜索计算机对社会的影响。Google 的搜索分析算法中这类技术比比皆是，而且面向几乎所有语言。

理解用户尝试理解用户的目的是为用户返回他们真正需要的结果，而不是他们在搜索语句中所说的东西。该技术基于一个世界级的本地化系统，外加先进的个性化技术，以及各种用户意图识别技术。

对本地结果的重视体现在他们的本地化工作中。同样一个查询语句在不同国家会返回不同结果，比如，查询 "bank]"，在美国返回的是银行，而英国则可能是 Bank Fashion 的服装连锁店，或者英国的银行，而在其它英语国家，如澳大利亚，加拿大，新西兰，南非，返回的则应仍旧是当地的银行。如果你在一些非英语国家查询这个词， 象埃及，以色列，日本，俄罗斯，沙特，瑞士，返回的结果将更有趣。就象 Football 在美国和英国表示不同的运动项目一样，同一个词在不同国家查询的结果可能截然不同。

个性化查询是 Google 另一项先进搜索技术，一个已经登录的用户，如果开通了 Web History 服务，随着他查询时间的增长，Google 会根据他的查询历史，自动调整返回的结果，比如，一个经常查询 Football 相关话题的人，会逐渐从 Google 得到更多足球相关的结果。如果你青睐某个购物站返回的结果，在以后的查询中，会从那个购物站得到更多结果。

在返回用户真正想要的结果方 面的另一个例子是，假如你搜索 "chevrolet magnum"，我们知道 Magnum 不是 Chevrolet 产的，是 Dodge 产的，Google 会自动返回 dodge magnum 的结果。还有一个例子，有人搜索 "bangalore"，不仅返回 Bangalore 这个城市的主页，而且返回 Bangalore 的地图，以及一些与 Bangalore 市景，交通相关的视频，这些视频会让你有身临其境的感觉。

本文国际来源：http://googleblog.blogspot.com/2008/07/technologies-behind-google-ranking.html

作者：ComSharp

英文：

Technologies behind Google ranking

7/16/2008 10:53:00 AM

In my previous post, I introduced the philosophies behind Google ranking. As part of our effort to discuss search quality, I want to tell you more about the technologies behind our ranking. The core technology in our ranking system comes from the academic field of Information Retrieval (IR). The IR community has studied search for almost 50 years. It uses statistical signals of word salience, like word frequency, to rank pages. (See "Modern Information Retrieval: A Brief Overview" for a quick overview of IR technology.) IR gave us a solid foundation, and we have built a tremendous system on top using links, page structure, and many other such innovations.

Search in the last decade has moved from give me what I said to give me what I want. User expectations from search have rightly increased. We work hard to fulfill the expectations of each and every user, and to do that we need to better understand the pages, the queries, and our users. Over the last decade we have pushed the technologies for understanding these three components (of the search process) to completely new dimensions.

When we talk about queries at Google, we use square brackets [ ] to mark the beginning and end of queries (see " How to write queries" by Matt Cutts), a notation I will use throughout this post. (Pages and search results change frequently, so in time, some examples used here may not behave as explained.)

• Understanding pages: Over years we have invested heavily in our crawl and indexing system. As a result we have a very large and very fresh index. In addition to size and freshness, we have improved our index in other ways. One of the key technologies we have developed to understand pages is associating important concepts to a page even when they are not obvious on the page. We find the official homepage for Sprovieri Gallery in London for the Italian query [galleria sprovieri londra], even though the official page does not have either London or Londra on it. In the U.S., a user searching for [cool tech pc vancouver, wa] finds the homepage www.cooltechpc.com even though the page does not mention anywhere that they are in Vancouver, WA. Other technologies we have developed include distinctions between important and less important words in the page and the freshness of the information on the page.
  

• Understanding queries: It is critical that we understand what our users are looking for (beyond just the few words in their query). We have made several notable advances in this area including a best-in-class spelling suggestion system, an advanced synonyms system, and a very strong concept analysis system.

Most users have used our spelling suggestion system at one time or another. It knows that someone searching for [ kofee annan] is really searching for Mr. Kofi Annan, and is prompted: Did you mean: kofi annan; whereas someone searching for [ kofee beans] is actually looking for coffee beans. Doing this internationally with very high accuracy is hard, and we do it well.

Synonyms are the foundation of our query understanding work. This is one of the hardest problems we are solving at Google. Though sometimes obvious to humans, it is an unsolved problem in automatic language processing. As a user, I don't want to think too much about what words I should use in my queries. Often I don't even know what the right words are. This is where our synonyms system comes into action. Our synonyms system can do sophisticated query modifications, e.g., it knows that the word 'Dr' in the query [ Dr Zhivago] stands for Doctor whereas in [ Rodeo Dr] it means Drive. A user looking for [ back bumper repair] gets results about rear bumper repair. For [ Ramstein ab], we automatically look for Ramstein Air Base; for the query query [ b&b ab] we search for Bed and Breakfasts in Alberta, Canada. We have developed this level of query understanding for almost one hundred different languages, which is what I am truly proud of.

Another technology we use in our ranking system is concept identification. Identifying critical concepts in the query allows us to return much more relevant results. For example, our algorithms understand that in the query [ new york times square church] the user is looking for the well-known church in Times Square and not for articles from the New York Times. We don't just stop at identifying concepts; we further enhance the query with the right concepts when, for instance, someone looking for [ PC and its impact on people] is in fact looking for impact of computers on society, or someone who searches for [ rainforest instructional activities for vocabulary] is really looking for rain forest lesson plans. Our query analysis algorithms have many such state-of-the-art techniques built into them, and once again, we do this internationally in almost every language we serve.

• Understanding users: Our work on interpreting user intent is aimed at returning results people really want, not just what they said in their query. This work starts with a world class localization system, and adds to it our advanced personalization technology, and several other great strides we have made in interpreting user intent, e.g. Universal Search.
  

Our clear focus on "best locally relevant results served globally" is reflected in our work on localization. The same query typed in multiple countries may deserve completely different results. A user looking for [ bank] in the US should get American banks, whereas a user in the UK is either looking for the Bank Fashion line or for British financial institutions. The results for this query should return local financial institutions in other English speaking countries like Australia, Canada, New Zealand, South Africa. The fun really starts when this query is typed in non-English-speaking countries like Egypt, Israel, Japan, Russia, Saudi Arabia, Switzerland. Likewise the query [football] refers to entirely different sports in Australia, the UK, and the US. These examples mostly show how we get the localized version of the same concept correctly (financial institution, sport, etc.). However, the same query can mean entirely different things in different countries. For example, [Côte d'Or] is a geographic region in France - but it is a large chocolate manufacturer in neighboring French-speaking Belgium; and yes, we get that right too :-).

Personalization is another strong feature in our search system which tailors search results to individual users. Users who are logged-in while searching and have signed up for Web History get results that are more relevant for them than the general Google results. For example, someone who does a lot football-related searches might get more football related results for [giants], while other users might get results related to the baseball team. Similarly, if you tend to prefer results from a particular shopping site, you will be more likely to get results from that site when you search for products. Our evaluation shows that users who get personalized results find them to be more relevant than non-personalized results.

Another case of user intent can be observed for the query [ chevrolet magnum]. Magnum is actually made by Dodge and not Chevrolet. So we present the results for Dodge Magnum with the prompt See results for: dodge magnum in our result set.

Our work on Universal Search is another example of how we interpret user intent to give them what they (sometimes) really want. Someone searching for [ bangalore] not only gets the important web pages, they also get a map, a video showing street life, traffic, etc. in Bangalore -- watching this video I almost feel I am there :-) -- and at the time of writing there is relevant news and relevant blogs about Bangalore.

Finally let me briefly mention the latest advance we have made in search: Cross Language Information Retrieval (CLIR). CLIR allows users to first discover information that is not in their language, and then using Google's translation technology, we make this information accessible. I call this advance: give me what I want in any language. A user looking for Tony Blair's biography in Russia who types the query in Russian [ Тони Блэр биография] is prompted at the bottom of our results to search the English web with:

Similarly a user searching for Disney movie songs in Egypt with the query [ أغاني أفلام ديزني] is prompted to search the English web. We are very excited about CLIR as it truly brings us closer to our mission to organize the world's information and make it universally accessible and useful.

I could go on and on showing examples of state-of-the-art technology that we have developed to make our ranking system as good as it is, but the fact is that search is nowhere close to being a solved problem. Many queries still don't get satisfactory results from Google, and each such query is an opportunity to improve our ranking system. I am confident that with numerous techniques under development in our group, we will make large improvements to our ranking algorithms in the near future.

I hope my two posts about Google ranking have made it clear that we live and breathe search, and we are more passionate than ever about it. Our fervor for serving all our users worldwide is unprecedented. We pride ourselves in running a very good ranking system, and are working incredibly hard every day to make it even better.

Posted by Amit Singhal, Google Fellow