Malaysian business community social network mapping on the web based on improved genetic algorithm

Abstract

The issues of community social network mapping on the web have been intensively studied in recent years. Basically, we found that social networking among communities has become a popular issue within the virtual sphere. It relates to the practice of interacting with others online via blogsphere, forums, social media sites and other outlets. Surprisingly, Internet has caused great changes to the way people do business. In this chapter, we are focusing on the networks of business in the Internet since it has become an important way of spreading the information of a business via online. Business networking is a marketing method by which business opportunities are created through networks of like-minded business people. There are several popular businesses networking organization that create models of networking activity that, when followed, allow the business person to build new business relationship and generate business opportunities at the same time. Business that increased using the business social networks as a means of growing their circle of business contacts and promoting themselves online and at the same time develop such a “territory” in several regions in the country. Since businesses are expanding globally, social networks make it easier to keep in touch with other contacts around the world. Currently, searching and finding the relevant information become a high demand from the users. However, due to the rapid expansion of web pages available in the Internet lately, searching the relevant and up-to-date information has become an important issue especially for the industrial and business firms. Conventional search engines use heuristics to decide which web pages are the best match for the keyword. Results are retrieved from the repository which located at their local server to provide fast searched. As we know, search engine is an important component in searching information worldwide. However, the user is often facing an enormous result that inaccurate or not up-to-date. Sometimes, the conventional search engine typically returned the long lists of results that saddle the user to find the most relevant information needs. Google, Yahoo! and AltaVista are the examples of available search engine used by the users. However, the results obtain from the search engines sometimes misrelated to the users query. Moreover, 68% of the search engine users will click a search result within the first page of results and 92% of them will click a result within the first three pages of search results (iProspect, 2008). This statistic concluded that the users need to view page by pages to get the relevant result. Thus, this will consume the time to go through all the result provides by search engine. From our experienced, the relevant result also will not always promise found even after looking at page 5 and above. Internet also can create the abundance problem such as; limited coverage of the Web (hidden Web sources), limited query interface: keyword-oriented search and also a limited customisation to individual users. Thus, the result must be organized so that them looks more in effective and adapted way. In previous research, we present the model to evaluate the searched results using genetic algorithm (GA). In GA, we considered the user profiles and keywords of the web pages accessed by the crawler agents. Then we used the information in GA for retrieving the best web pages related to the business communities to invest at the Iskandar Malaysia in various sectors such as education, entertainment, medical healthcare etc. The main objective of this chapter is to provide the user with a searching interface that enabling them to quickly find the relevant information. In addition, we are using the crawler agent to make a fast crawling process and retrieve the web documents as many as it can and scalable. In the previous paper, we also using genetic algorithm (GA) to optimize the result search by the crawlers to overcome the problem mention above. We further improve the GA with relevance feedback to enhance the capabilities of the search system and to find more relevant results. From the experiments, we have found that a feedback mechanism will give the search system the user’s suggestions about the found documents, which leads to a new query using the proposed GA. In the new search stage, more relevant documents are retrieved by the agents to be judged by the user. From the experiments, the improved GA (IGA) has given a significant improvement in finding the related business communities to potentially invest at Iskandar Malaysia in comparison with the traditional GA model. This chapter is organized as follows. Section 2 defined the problem that related to this chapter. Section 3 is details on improved genetic algorithm and section 4 are the results and discussion. Section 5 explains the results and discussion of this chapter and Section 6 presented the case study. Finally, section 7 describes the conclusion.