Paper Presentation & Seminar Topics: A Community-Based Approach to Personalizing Web Search

A Community-Based Approach to Personalizing Web Search

Community web based search emphasis the users role in making of the content in the websites. This ideas emphasizes the importance of the sharing, community, and participation in the web search. These ideas have influenced research into how the search behavior of communities of like-minded users can be harnessed and shared to adapt the results of a conventional search engine according to the needs and preferences of a particular community. Ideally, this leads to an improved personalized search experience that can deliver more relevant result pages that reflect the experiences of a community of users, effectively forming collective search wisdom.
This collaborative Web search approach promotes the idea that community search activities can provide a valuable form of search knowledge and that facilitating the sharing of this knowledge between individuals and communities makes it possible to adapt traditional search-engine results according to the community’s needs.
Over the past few years, current Web search engines have become the dominant tool for accessing information online. However, even today’s most successful search engines struggle to provide high-quality search results: Approximately 50 percent of Web search sessions fail to find any relevant results for the searcher. The earliest Web search engines adopted an information-retrieval view of search, using sophisticated term based matching techniques to identify relevant documents from repeated occurrences of salient query terms. Although such techniques proved useful for identifying a set of potentially relevant results, they offered little insight into how such results could be usefully ranked. How then should documents be ranked and ordered? Some researchers1,2 solved this problem when they realized that ranking could be greatly improved by evaluating the importance or authoritativeness of a particular document. By analyzing the links in and out of a document, it became possible to evaluate its relative importance within the widerWeb. For example, Google’s famous Page Rank metric assigns a high page-rank score to a document if it is itself linked to by many other documents with a high page-rank score, and it iteratively evaluates the page-rank scores for every document in its index for use during results ranking.