The web search efforts started with the application of classical content-based information retrieval techniques to the web. But due to the commercial pressures, spamming became the major threat to the success of the content-based techniques. Attention was then diverted to exploit the hypertextual structure of the web. But, both the content based approach as well as the hyperlink based approach are objective ones, which are totally dependent on the effectiveness of their “feature extraction” mechanisms, with no apparent consideration to the preference of the searcher. In this chapter, a “user satisfaction” guided web search procedure is proposed. We calculate the importance weight of each document viewed by the user based on the feedback vector obtained from his actions. This document weight is then used to update the index database in such a way that the documents being consistently preferred go up the ranking, while the ones being neglected go down. Our simulation results show a steady rise in the satisfaction levels of the modeled users as more and more learning goes into our system. We also propose a couple of novel additions to the web search querying techniques. The user feedback obtained on the search results is also utilized for evaluating the performance of the search engine that supplied the results to the user. We also include a brief overview of the existing web search techniques.