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编程的原理和实践 Programming Principles and Practice 课程1
“隐式网络”出现的影响：隐式网络服务使用户间接地被追踪到，他们的相互作用(如点击行为)作为后续营销的基础。
“万维网可以看作是世界上最大的数据库”,(van Wel and Royakkers,2004)。蒂姆·伯纳斯-李在1989年发明并发展起来的万维网(网络)如今被数以百万计的人用于每天的生活和工作。网络是一个相互联系的体系结构，通过互联网访问超文本文档。网络允许用户访问几乎任何话题的信息,一个程序页面的格式组成文本、图片和视频。
现在网络已经存在了很多年，web 2.0技术的运用也越来越成功,用户正在寻找一个更有意义的网络。这带来了隐式网络的想法。虽然“隐式网络”这个术语是2007年(发明),这一概念已经使用了很长一段时间。隐式网络现在被用涵盖性术语，共享相同的意义。 Coursework 1
Implications from the advent of the “implicit web” where users are profiled indirectly and their interactions (e.g. click behaviour) are used as the basis of subsequent marketing.
“The World Wide Web can be seen as the largest database in the world“, (van Wel and Royakkers, 2004). Invented and developed by Tim Berners-Lee in 1989, the World Wide Web (the Web) is used by millions of people everyday. The web is an architecture of interconnected hypertext documents that are accessed via the internet. The Web allows users to access information on almost any topic of their choice, in the format of a web page consisting of text, images and videos.
Now that the web has been around for many years as well as the increasing success of Web 2.0 technology, users are now looking for a more meaningful web. This brings about the idea of the implicit web. Although the term “implicit web” was coined, (invented) in 2007, the concept has been in use for quite some time. Implicit web is now used as an umbrella term for similar terms that essentially share the same meaning.
Expressions that have been found during research included web personalisation, web data mining, (or web mining), and implicit interaction. Atterer, Wnuk and Schmidt, (2006) define implicit interaction to be “the observable interaction behaviour of the user that is not done consciously while focusing on reaching a goal”. This suggests that implicit web recognises and understands that users leave behind a trail of the links that they clicked on and what web pages they have looked at and for how long. The goal that the user wanted to reach could be anything from finding out the medical treatment of an illness, to shopping on the World Wide Web, to reading current news stories.
Web personalisation or web data mining is present when an online user clicks on a link, spends time reading a webpage or fills in an online form. Such interactions can be recorded so that more meaningful and relevant information can be made available to the user. Actions such as clicking hyperlinks, mouse movements on the screen, scrolling activity and general user browsing allows implicit information to be recorded about a specific user. The reason it is now termed as implicit is that the user unintentionally gives information about what they are interested in, without even realising. The difference between implicit and explicit information is illustrated in the following example.
A user types in the keyword, “cars” into a Google search. This would classed as being explicit, as the user is explicitly (clearly) asking for information on cars. When the results appear, it shows at the top, the most popular links that other users clicked on when they searched the same keyword. This is the implicit information that they (the previous users) collectively left behind for the user in this example to view, in the form of search results. The more clicks (and therefore more popular) a link is, the higher it moves up in rank when results are shown. So, if the user clicks on a link which navigates to the Disney website, for the film Cars, the presence of a web data mining tool would then “know” from the user's click behaviour that the user is interested in the film Cars, as opposed to cars in general. If the user realises that they didn't want to view that site, and navigates back to the results to click on another link that is on latest car reviews, and spends time looking at that website, the web personalisation tool can recognise that the user wants information on latest car reviews as apposed to the film Cars. This implicit behaviour can then be logged to retrieve more relevant information on the car reviews that the user wants.
Atterer, Wnuk and Schmidt, (2006) also suggest that although implicit interactions can be very useful in providing a faster and better web, it can also be misleading. Using the above example, if the user clicked on the website for car reviews and then went away from the computer to answer a telephone call, the action of the user staying on that page could be misinterpreted, in that the user is interested in that topic, when he/she may not be. Implicit interaction may not always be completely certain, (as in this instance), which can be a disadvantage. Incorrect interpretations of implicit interaction could work against a company, as the customer could get annoyed, and not purchase any items if the website does not make the proper recommendations or personalisation for the customer's specific needs.
One of the implications of the implicit web is that online businesses, (such as Amazon) can use the implicit interactions unintentionally left by previous users to provide customers with personalised marketing and smart recommendations. This information is collected by recording a customers browsing history, past purchases and purchases of other shoppers, to try to get the customer to buy the product recommended product(s) from them. “Web personalization is any action that makes the Web experience of a user personalised to the user's taste and preferences”, (Srour, Kayssi and Chehab, 2007).
Figure 1 below, illustrates that when a user logs onto Amazon's website, it shows the customer personalised recommendations based on previous purchases. This can be termed as a social recommendation, due to its analytical content which provides a statistical reason as to why this item should be purchased. At the same time, it can also be classed as a personalised recommendation as it is based on an item that the customer recently clicked on. Figure 1
Figure 2 shows item recommendations based on new releases, where clicking on the hyperlink “Why is this recommended for you?” navigates the user to their purchasing history. This can also be classed as a personalised recommendation as it is based on a customer's previous behaviour. Figure 2
Implicit web can be very useful for highlighting top stories on news websites. The Sky News website has a “Most Read” section on its homepage. This is another example of implicit web, where many users have read news stories, and the ones with the highest click rates are posted on the “Most Read” section of the homepage to inform users of the most read (and therefore possibly the most interesting or important) stories at that time. Another website that makes use of the implicit web is mysportsnet.ca. It customises the content of the website as per the user's behaviour. It also uses this information to display relevant banner advertisements depending on the sport the user seems to be interested in. This can be used as an effective e-business collaboration tool where businesses can amalgamate their resources and complement each other in such a way that is beneficial to both organisations. For example, if a user is interested in baseball, the advertisements that are shown are baseball related. If the user is exposed to these, there is always potential that one of those advertisements will interest them, which could then lead onto a sale of a baseball bat, ball and glove set.
One of the main ethical issues with regards to the implicit web is that of respecting user privacy. Van Wel and Royakkers (2004), state that it is unethical for a user's information to be obtained and used without their knowledge or consent. Mysportsnet.ca allows for this, where the site is powered by a desktop application that is used to track the content read online. The privacy policy states that it doesn't capture personal information and the application can be easily turned off and uninstalled.
Van Wel and Royakkers 2004, also emphasise that if anonymous data of online users and their web-behaviour is collected, the group profiles created from this information for mass customisation would not contain personal data, therefore alleviating the privacy issue. However, if the data collected was used as personal data, this would lead to the unfair judgement of people, a term they define as de-individualisation.
If the implicit web could overcome ethical issues such as user privacy, misuse of personal information and de-individualisation, the implicit web could become a world-wide phenomenon, which would change the way we use the World Wide Web. Users would be exposed to the things that they are specifically interested in, and personal information would also remain undisclosed. If an online shopper finds websites that that suits their needs, the user's experience of the web can be further enhanced.
Time spent on research on the web can be cut down drastically with the advent of the implicit web as it will help find relevant information and websites based on what the user has been searching and reading.#p#分页标题#e#
The implicit web can help to improve the intelligence of search engines and can also be used for marketing purposes by analysing a web user's online behaviour to aid marketing techniques. Clustering information on customers allows businesses to retain existing customers by providing a personalised service. In conclusion, the implicit web reveals the dynamic side of human knowledge by recording how users access data, services, and links and engages in collective human behaviours. The implicit web is not part of the semantic web, but they are closely related. The Semantic Web constructs a conceptual model whereas the implicit web constructs a behavioural model of the World Wide Web, where actions speak louder than words. |
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