With the development of internet and wireless technologies, location based search is among the most discussed topic in current era. To address issues of location based search a lot of research has been done but it mainly focused on the specific aspects of the domain like most of the studies focused, on the search of nearby restaurants, shopping malls, hospitals, stores etc., by utilizing location of users as searching criteria. Problem with these studies is that users might not be satisfied by their results and the sole reason behind this might be the absence of user preferences in the search criteria. There exists some studies which focused user preferences along with user location and query time and proposed some frameworks but they are only limited to stores and their research cannot be scaled to other points like schools, hospitals, doctors , petrol pumps, gas station etc. Moreover there exist scalability issues in their recommended algorithms along with some data credibility issues in their public evaluations strategies. Our proposed research is going to present a novel location based searching technique not only for stores but for any point. The presented solution has overcome issues faced in previous research studies and possesses capability to search for “K” nearest points which are most preferable by user, by utilizing searching time as well as query location. Our research has proposed two feedback learning algorithms and one ranking algorithm. To increase the credibility of public evaluation score, system have utilized Google ranking approach while calculating the score of the point. To make user recommendations nonvolatile along with improving recommendations algorithm efficiency, proposed system have introduced item to item collaborative filtering algorithm. Through experimental evaluations on real dataset of yelp.com presented research have shown significant gain in performance and accuracy.