Data Mining and Web Personalization - Essay Example

Data Mining and Web Personalization College Introduction to Web Personalization Web personalization is a business solution that is becoming more involved as technology advances and consumers are able to be more selective in how they pursue business relationships. Web personalization is any action that customizes the content or services provided by a web site to a particular user or users. This system allows companies to provide users with the content and products that they need without having to specifically request the information from the site (Eirinaki & Vazirgiannis, 2003). Business Problems One of the major business problems in the twenty-first century is the increase in e-commerce and a move away from traditional brick-and-mortar stores. In the past, business owners were able to meet their customers and remember their wants and needs. Because of advances in technology, the Internet is now a major component of conducting business. While this creates many positive advantages, it is also more difficult for business owners and executives to determine the wants and needs of consumers. Data mining as used in web personalization allows business owners to concentrate on solving many problems and making e-commerce more of a viable business option. One of the goals of web personalization can be to convert potential buyers into buyers by personalizing and customizing web site structure and content. Web site design and usability can be greatly improved by using web personalization. The personalization process uses information that can change the structure and content and generate better results. Web personalization can also improve customer loyalty by providing content that is targeted to consumer wants and needs. Another goal for web personalization is to increase cross-selling by changing the structure and layout of sites as suggested by data mining. This can be accomplished by using information to suggest additional purchases to complement user tastes and preferences. Finally, web personalization can also be used to help individual users quickly find content that they will be able to use quickly. Components of Web Personalization There are several components necessary for successful web personalization. These components include categorizing and preprocessing web site data, extracting existing correlations between and across these data sets, and finally, determining the actions that should be taken by the system based on recommendations by the web personalization system (Mobasher, et al., 2000). In addition, there are four types of web data that can be collected and used for web personalization. Content data can include text, pictures, or database information. User profile data gives information about the users of a particular site. This can include operating system information, age, race, country of origin, ethnicity, education, and user preferences. Usage data describes how visitors used a web site and can include file accession logs, IP addresses, peak access times, and the addresses of referring web sites. Finally, structure data show how web site content is organized. This can include metatags, hyperlinks from one page to another, and the type of file format used on the web page (Srivastava, et. al., 2000). Web personalization can be categorized into four distinct classes, the first class being the most simple and the fourth class being the most complex. Memorization is the simplest category of web personalization. It is a very popular method of extracting information about web site users. Information is stored and then used later to remember and greet the user. Customization is the next category of web personalization. This is slightly more complex than memorization. Here, registration forms are used to collect user information and preferences. This information is then used to customize the layout and content of a web page. The third, more complicated, category of web personalization is guidance or recommender systems. The guidance-based system will attempt to make recommendations on hyperlinks that are related to user interests. This is usually server-based and can be collected through registration forms and browsing history. Finally, the most advanced category of web personalization is task performance support. This is a client-side personalization system that uses an assistant to make accessing relevant information less difficult. The Process of Web Personalization Web personalization is an involved process consisting of five modules that contribute to the overall effectiveness of the personalization program. The first module is user profiling. In this module, specific information is gathered about each web site user. This information can be collected with or without the user having knowledge of the collection process. A user profile is compiled and may include demographic information and characteristics of user behavior. This information is then used to customize the web site's structure and information to the needs of specific users. The second module is web log analysis and web usage mining. In this step, information stored in the web usage logs is analyzed and processed using data mining techniques. This module serves three important purposes. The first is to derive interesting site usage patterns by using the statistics available. The second is to group users together according to their usage behavior. The third purpose is to discover connections between web pages and user groups. Content management may be considered one of the most important aspects of web personalization. In this module, web site content is classified into categories that make it easier for users to access and use the available information. Web site publishing is the fourth module of the web personalization process. This involves using a publishing mechanism to present information to users in an organized and uniform way. Web publishing can be achieved using many different mechanisms. Finally, information acquisition and searching involves searching the Internet for interesting content and uploading it to a site's server. This content is then organized into categories by theme (Eiranaki & Vazirgiannis, 2003). Data Mining Technology and Web Personalization Web personalization uses many types of data mining technology to accomplish its intended goals. This involves analyzing data and computing recommendations based on that data. The first type of data mining used in web personalization is content-based filtering. This system makes recommendations based on items that users were interested in on past site visits. One of the major advantages of content-based filtering is the fact that items that are brand new to the site can be recommended. This does not rely on previous ratings or purchase statistics. Collaborative filtering is somewhat similar to content-based filtering, with the exception of how recommendations are made. Instead of making recommendations based on the past behavior of individual users, this type of system suggests items based on those enjoyed by users with similar interests. This system relies on user ratings as recorded by the site. These ratings can be based on concrete information such as user ratings, customer satisfaction surveys, or previous purchases. It can also be based on the browsing behavior of past users, which is a more implicit approach to making recommendations. Computing recommendations can be accomplished with one of two methods. In the lazy learning phase, user behavior is stored until a new user can then be compared to these past users. In the eager learning phase, clusters, profiles, decision tress, and other types of data mining are used to summarize user interests. Rule-based filtering is used to customize products on e-commerce sites. Users are asked to answer a series of questions about their preferences until a list of customized items is generated. This type of filtering relies on the heavy planning of question and answer combinations as well as expert customizations (Nasraoui, n.d.). Firms Utilizing Data Mining and Web Personalization Techniques E-commerce organizations are now using web personalization as a strategic business and marketing tool. Because this multi-billion dollar industry is rapidly becoming more competitive, it is important for e-commerce organizations to use every available marketing tool to attract and retain new and repeat customers. Some popular choices for personalization include Kefta, ChoiceStream, and Axtive Software. Many global organizations utilize the data mining techniques associated with web personalization. Yahoo is one of the most recognizable of these organizations. Yahoo has integrated a search engine, electronic mail, news, weather, and other features into a portal that is customized to each user. This customization has now developed into a feature called "My Web." My Web gives users several personalization capabilities. Visitors can save web pages to their own personal archive, search the archive, and share saved web pages through electronic mail and Yahoo's instant messaging system. Yahoo toolbar has also been integrated with My Web and allows users to save web pages to their archive with one click. In addition, Yahoo has also introduced a personalized portal that allows users to create web logs and network with others from around the globe. This portal is now in the beta testing stage but will be integrated with My Web when this stage is complete. Yahoo is using these personalized features to attract users to its site and keep them from visiting competing sites (Hicks, 2005). Google has also gotten involved with web personalization with the introduction of its personalized search results option. Users can create a profile based on their interests and then have the option of customizing their searches to those interests. Google is also offering Google Web Alerts, which alert users to changes in Google search results. One problem with the personalized search feature is that listing multiple interests in the user profile makes it difficult for the search engine to compile relevant results. Another issue related to Google's web personalization is the concern for individual privacy. Google uses cookie data to track user preferences and browsing history, which generates concerns for the privacy of personal information (Sullivan, 2004). Amazon uses a recommender system to personalize item recommendations for each user. This type of system bases the recommendations on two key components. The first is the past buying habits of the individual user. The second is the buying habits of other site users. This is successful because it does not require the user to take any additional steps when browsing the site. Because the site has millions of users, the recommender system is better able to determine what types of books are similar to each other. If a user purchases a mystery novel, the system may recommend mystery novels by different authors. Amazon also uses this data to create hyperlinks within each individual item page. This is even more successful than item recommendations because it is incorporated into a user's normal browsing behavior (Ouellette, 1999). Web Personalization Challenges While data mining has made web personalization a successful marketing tool, there are also several challenges associated with this type of technology application. One of those major challenges is dealing with rapidly changing user interests and preferences. This requires that the mining phases that are now offline be changed to a framework that is fully online. This can only be accomplished with a complicated system of single-pass evolving stream mining techniques. Scalability is also a major challenge for organizations that wish to incorporate web personalization into their marketing strategies. This means that the personalization programs need to be efficient in the amount of storage they require. Accuracy is a major concern for organizations using web personalization technology. Because this process is not completely exact, businesses run the risk of upsetting customers with inappropriate recommendations. What some users might consider helpful, other users may consider inappropriate or unethical. One approach to dealing with the issue of accuracy is to add an additional data mining phase that is separate from the data mining conducted to determine user preferences. This data mining phase would be concerned with coming up with a more accurate model for making recommendations. Data collection and preprocessing are imperfect components of the web personalization process. When cookies or registration forms are missing, it is difficult to identify users correctly (Berendt et al., 2001). Integrating multiple sources of data is also a concern for those involved with web personalization techniques. More useful applications can be developed using content, usage, and structure data mining to provide more personalized results. Finally, privacy is one of the biggest concerns of organizations using this technology and users on the receiving end of the technology. Some users do not like to give personal information due to fear that it could be used for criminal purposes. Consequently, some users may not visit sites that use cookies, or may block cookies altogether, making the sites they visit unable to collect personalization information. When users do agree to give out personal information, there is no guarantee that the information will be kept private and not shared with other organizations. As a result of these concerns, future web personalization efforts will focus on following new privacy standards, making more accurate recommendations, dealing with rapidly evolving user interests, and more reliable data collection and processing (Nasroui, n.d.). Analysis of Data Mining and Web Personalization Data mining, as used for web personalization purposes, has proven to be a valuable tool for global business organizations. This exciting technology allows businesses to take a more proactive approach to customer relationships. Instead of reacting to dissatisfied customers, businesses are now able to be proactive in their efforts to maintain a high standard of customer satisfaction. Web personalization can be considered successful in some ways and unsuccessful in others. Web personalization has been successful because of the ability for users to retrieve news, weather, sports, and other information that is relevant to their interests or location. After visiting web sites with personalization options, it is clear that these tools can enhance the web experience for a majority of users. In addition, the organizations utilizing this technology appear to be more dedicated to providing users with relevant content than sites that provide no personalization options. Web personalization has also been proven a successful strategic marketing tool. Those sites that have personalization options have quickly grown more popular than competitors without these options. In addition, among sites that have these options, the sites with the easiest to use and least intrusive personalization features have inched ahead of their competitors whose tools are bothersome to users or require extra effort to achieve personalization. On the other end of the spectrum, web personalization can also be considered unsuccessful in some ways. Some data mining tools are bothersome to users. An excellent example of this is when sites have pop ups that interrupt users from finding the content they are looking for. Some web personalization options create extra work for the user, which defeats the entire purpose of personalization. Personalization should be a tool used to speed customer's access of relevant and desired information. When data mining tools interfere with the customer's browsing experience, it creates the potential for a lack of customer loyalty or the loss of a repeat customer. The privacy issue is also a major concern when computer hacking and identity thefts are considered. While providing personal information for personalization purposes can create excellent site experiences, it also creates the potential for this information to be used in a fraudulent manner. It is only with careful consideration of these issues that web personalization can become a marketing tool that is useful for both organizations and a majority of consumers. Summary The research suggests that data mining as related to web personalization is still a relatively new concept. While this is a complex process, it can also simplify the experiences of users around the globe. Organizations should consider the needs of their customers, the security of information, and the accuracy of recommendation systems before fully implementing any personalization tool. With proper implementation, web personalization can continue to be a cutting-edge marketing tool that satisfies both businesses and consumers alike. References Berendt, B., Bamshad, M., Spiliopoulou, M., & Wiltshire, J. (2001). Measuring the accuracy of sessionizers for web usage analysis, In Workshop on Web Mining, at the First SIAM International Conference on Data Mining, 7-14. Eirinaki, M. & Vazirgiannis, M. (2003). Web mining for web personalization." ACM Transactions on Internet Technology, 3(1), 1-27. Hicks, M. (2005). Yahoo launches personalized web search. E-Week [Online Version]. Available: Nasraoui, O. (n.d.). World Wide Web personalization. University of Louisville Department of Computer Engineering and Computer Science. Available: Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic personalization based on web usage mining. Communications of the ACM, 43(8), 142-151. Ouellette, T. (1999). Web personalization. Computerworld. [Electronic Version]. Available: Srivastava, J., Cooley, R., Deshpande, M., & Tan, P.N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2), 12-23. Sullivan, H. (2004). Google loses search tabs, gains web alerts. Search Engine Watch [Electronic Version]. Available: Read More