Web search enhancement by mining user actions

Search engines are among the most popular as well as useful services on the web. There is a need, however, to cater to the preferences of the users when supplying the search results to them. We propose to maintain the search profile of each user, on the basis of which the search results would be determined. This requires the integration of techniques for measuring search quality, learning from the user feedback and biased rank aggregation, etc. For the purpose of measuring web search quality, the “user satisfaction” is gauged by the sequence in which he picks up the results, the time he spends at those documents and whether or not he prints, saves, bookmarks, e-mails to someone or copies-and-pastes a portion of that document. For rank aggregation, we adopt and evaluate the classical fuzzy rank ordering techniques for web applications, and also propose a few novel techniques that outshine the existing techniques. A “user satisfaction” guided web search procedure is also put forward. Learning from the user feedback proceeds in such a way that there is an improvement in the ranking of the documents that are consistently preferred by the users. As an integration of our work, we propose a personalized web search system.