On Personalized Web Improvement - Literature review Example

?Personalised Web Literature Review [ID Introduction Personalised web services are services that are designed to be tailored to the users' individual needs. A common example is a personalised web portal, such as IGoogle. These portals become the default way of the user to relate to the Internet: They choose applications, news services, etc. The array of ways that a web service can be thus personalised is staggering. “Building personalized Web applications, i.e. those applications that are responsive to the individual needs of each user, is a challenging task. It involves a myriad of different technologies that range from simple database views to software agents and collaborative filtering algorithms” (Rossi et al, 2001). The array of ways that a program can offer personalisation seems endless (Rossi et al, 2001). However, there has been a routine failure to actually examine the process of creating a personalised web (Rossi et al, 2001). Essentially, most personalised web services end up providing a template, which can have elements selected or unselected, changed cosmetically (say in color), cropped, etc. Users need personalised web portals and services because they allow users to get the information they need quickly. Many users need to constantly pop online to check only a few things: A favorite news site, an e-mail account, Twitter and Facebook, for example. Opening four tabs in a browser to do that is absurd. A personalised web portal lets them quickly check what they need and move on. There are numerous other advantages to the end user, of course. The issue is that there is an inherent tension between personalisation and security (Lee and Cranage, 2010). As information becomes personalised, particularly if that information has to be stored on a remote server (like Google), the risk of someone's private data being stolen increases tremendously. If these portals end up governing the entire way that people surf the Net and use electronic services, then they become immensely valuable for marketers, spammers, hackers and phishers. Advantages of Personalised Webs Personalised webs have seen use in dozens of arenas. As noted, the most common personalised web services are portals or applications that govern the way one relates to the Internet based on user input. Facebook, for example, is a modular system: It has its core functionality that can be changed according to the individual need of the user, particularly in terms of what information is being shared and what social networking features one uses, and then first and third-party applications can be installed to allow more interaction. Facebook can include news or stocks tickers, games, etc. Of course, Facebook's recent problems with privacy management also show some of the problems with these approaches (Vascellaro, 2010). But there are innumerable other, more specialised, personalised web services. Pl@nteInfo is an agricultural and crop management personalised system (Jensen et al, 2000). “[T]he farmer and adviser subscribers are very dedicated users. Both the activity patterns and the preferences of subjects in the system are significantly different between these subscriber types, with farmers generally searching specific advice and advisors using the system to keep their knowledge up-to-date” (Jensen et al, 2000). Similarly, RecOrgSeed has real potential to advance democracy in the agricultural domain and help recommend organic seeds to growers (Markellos et al, 2009). In fact, one of the major advantages of personalised web services is the increasing array of options provided to businesses. TV listing services are likely to be increasingly popular in the digital age. “The Internet has brought unprecedented access to vast quantities of information. However, in recent times, the problem of information overload has become more and more marked, and we are now reaching a point where it is becoming increasingly difficult to locate the right information at the right time” (Smyth and Cotter, 2005). Personalised webs are thus designed to resolve the information overflow issue both in terms of finding the data in a smart way customised to the user experience and presenting it in a way that the user will appreciate and digest with maximal efficiency (Smyth and Cotter, 2005). Smyth and Cotter (2000) offer the example of personalised television, which offers and selects channels and programs preferable to the user. How can a television system be a personalised web experience? Simple: The distinction between web and television is rapidly declining. The same backbones that deliver web service can deliver Internet service, whether it be satellite, cable, DSL, or other infrastructure. Comcast, AT&T and other providers are increasingly bundling television and Internet services, and making it easy for people to program DVRs on their cell phone or laptop remotely, order pay-per-view services and watch them either on the television or on the laptop, get streaming access, etc. Personalised TV services, then, can seek out shows that someone may be interested in, automatically record favored shows, provide reminders for programs that one wants to see, etc. Improvement in this vein will require smarter analysis of how viewers watch shows, such as window-in-window viewing that is increasingly common, and content matter, which complex tags that could be cloud-and-crowd sourced to determine what shows are like each other in what respects. Tan et al (2004) discuss a personalised web intelligence, the Flexible Organizer for Competitive Intelligene (or FOCI), that seeks out information portfolios personalised to the individual user. It is, in a sense, personalised automation. “FOCIs personalisation increases the degree of matching tremendously after a reasonable number of operations. In addition, the personalised portfolios can be used to track and organise new information with a good level of performance” (Tan et al, 2004). FOCI is one of the key ways that personalised webs will improve themselves in the near future, as it provides a way to find and present the data without overhead on the server or client side. One problem with present personalised web experiences is that they often are not “rich multimedia services” (O'Keeffe and Wade, 2009). But O'Keeffe and Wade recommend a system they have developed for combining content with services to improve not only efficiency but ease of use by creating adaptive media services. Indeed, personalised webs will not improve in any consistent manner until Of course, personalisation isn't just for the user. Websites want to target advertisement and offers very specifically: If they could do so well, they could actually make money by informing the right client about the right deals. This would also make advertisements less onerous: No one complains about advertisements that are on point to the message of a site. Doyle (2009) discusses mechanisms for personalised offers: Implemented properly, this could allow advertisers to make money without offending viewers. Personalised web services could even be used to help treat alcohol intervention (Bewick et al, 2008)! The intervention mechanism Bewick et al (2008) tested was able to reduce the amount of alcohol drank in one sitting in UK university students: “Delivering an electronic personalised feedback intervention to students via the World Wide Web is a feasible and potentially effective method of reducing student alcohol intake”. This points to a way to improve personalised web experiences: Giving feedback on decisions, allowing branching trees of content provision. Security Threats Personalisation and privacy, paradoxically, are at odds. To personalise, a program or administrator has to know what one wants, which requires personal data. “[P]ersonalised services lead to positive customer responses such as increased willingness to disclose personal information and make purchases. However, another stream of research emphasizes negative outcomes of personalisation – namely, privacy concerns surrounding the use of personal information and associated negative effects on behaviour” (Lee and Cranage, 2010). Facebook has run into extreme and prominent problems with this tension. Facebook runs more effectively as a business and as social networking if more information is shared: This has lead Facebook to err on the side of more rather than less information. But Facebook privacy concerns are reaching a peak (Vascellero, 2006). Personal data can be harvested by spammers, scammers and phishers: A lot of information, such as what bank one uses, what one's first pet was named, what school one went to, one's mother's maiden name, etc. can be found on Facebook alone, and once that information is known, it becomes very easy to construct mechanisms to get the rest of it. Similarly, stalking concerns, both cyber and real-world, are a serious issue. And with Facebook, people have different uses. Some people are afraid to post candidly on Facebook because they have a job, or because their friends have a job, and the managers or executives at that job also use Facebook. It is hard to separate people on Facebook into “Work Friends”, “Coworkers”, “School Friends”, etc., and to constantly monitor what one says and does. Luckily, Lee and Cranage (2010) found that, with proper design and assurances, the fears of customers can be assuaged. “[E]nhancing privacy assurance increases the perceived usefulness of services and decreases customer privacy concerns. Moreover, customer behavioural responses are positively related to the perceived usefulness of services and negatively associated with privacy concerns” (Lee and Cranage). Yet the question remains: How can the tradeoff between personalisation and privatisation be broken? Wang and Kobsa (2007) offer some tentative solutions. They recommend a system that “dynamically select personalization methods during runtime”, which allows instant customer feedback on privacy preferences (Wang and Kobsa, 2007, 157). They use directory components to act as a filter, feeding out boolean expressions that are in fed into a selector, meaning that there are three separate levels of privacy creation (Wang and Kobsa, 2007, 164). Different techniques can be matched to different needs: Demographic information can be saved under clustering techniques, while rule-based reasoning can be used for inputs like food preferences for a hypothetical food selection personalised web (Wang and Kobsa, 2007, 165). Cross-session log data can be automatically saved and controlled for. Wang and Kobsa's system is scalable across many different levels (2007, 166). Conclusion Improving personalised webs, it seems, will come from a few angles. 1. Resolving the personalisation-security dilemma 2. Using automated systems to not only improve privacy and reduce overhead but also provide real-time systems 3. Genetic algorithms and feedback mechanisms 4. Personalisation not only of information but also of formating of information Literature Bewick, BD., Trusler, K., Mulhern, B., Barkham, M., and Hill, AJ. 2008, “The feasibility and effectiveness of a web-based personalised feedback and social norms alcohol intervention in UK university students: A randomised control trial”, Addictive Behaviors, Vol. 33 No. 9. Doyle, S. 2009, “Personalised web offer management”, Journal of Database Marketing & Customer Strategy Management, 16, 51–56. Heitmann, B., Kim, JG., Passant, A., Hayes, C. and Kim, H-G. 2010, “An architecture for privacy-enabled user pro?le portability on the Web of Data”, Available at: http://ir.ii.uam.es/hetrec2010/res/papers/hetrec2010_paper_03.pdf Higel, S., Lewis, D., and Wade, V. 2005, “Realising Personalised Web Service Composition Through Adaptive Replanning”, Lectures Notes in Computer Science, Vol 3762. Jensen, AL, Boll, PS, Thysen, A and Pathak, BK. 2000, “Pl@nteInfo® — a web-based system for personalised decision support in crop management”, Computers and Electronics in Agriculture, Vol. 25 No. 3, February. Lee, CH. And Cranage, DA. 2010, “Personalisation–privacy paradox: The effects of personalisation and privacy assurance on customer responses to travel Web sites”, Tourism Management, January 18. Markellos, K, Markellou, P, Liopa-Tsakalidi, A, and Staurianoudaki, M. 2009, “Personalised web services for agricultural domain: a case study for recommending organic seeds to farmers and growers”, International Journal of Electronic Democracy, Vol. 1 No. 2. McLoughlin, C. and Lee, MJW. 2010, “Personalised and self regulated learning in the Web 2.0 era: International exemplars of innovative pedagogy using social software”, Australasian Journal of Educational Technology, Volume 26 No. 1, 28-43. 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Vascellaro, JE. 2010, “Facebook Grapples with Privacy Issues”, Wall Street Journal, May 19. Wang, Y. and Kobsa, A. 2007, “Respecting User's Individual Privacy Constraints in Web Personalization”, UM07 – 11th International Conference on User Modeling, 157-166. Read More