Recommendation engines are the system, which works as a information filter and predicts the requirement of the user on preferable search. It is very popular and implementedthese days on various web products including ecommerce, social networking site, and search engines. They collaborate information and build a information model from variousactivitiesperformed by the user, predicts related information and suggest them accordingly. Also it assumes the other interest that a user may have. It works on a algorithm which stores the users interests and show them what they like to see. The main problem with recommendation engines are sometimes the predictions are out of the pool, as the engine is based on algorithm, due to user activity it confuses and result out inappropriate results. The paper explains the strength and techniques of the recommendation engines and discussing issues and there solutions. © 2016, International Journal of Pharmacy and Technology. All rights reserved.