This study proposed a Collaborative Filtering (CF) algorithm that generates and recommends a list of alternate keywords based on the keywords originally set by a user when searching documents from an online search engine. The proposed CF algorithm incorporates the similarities among keywords associated with the documents and provides crucial recommendations that may not be considered by the user. The purpose of the proposed CF algorithm is to reduce the efforts in identifying appropriate keywords set to allocate the desired documents more efficiently. The traditional search algorithms strive to provide closely-matched results to the keywords specified by the user. However, the most exceptional search engine would not provide good quality results if the original keywords selected by the user were not suitable. Therefore, the proposed system aims at suggesting a set of alternative keywords generated based on the user's original keywords to help a user in his/her subsequent search activities. The proposed system is expected to improve search accuracy and effectiveness for a novice user in the field of interest. For experienced users, with the fast-growing availability of information online, who may not be aware of the most updated/critical keywords, the proposed system is also expected to improve search efficiency. Furthermore, the proposed system is flexible and can easily be integrated with other search algorithms to improve search results.