The users helped by the search engine for online transactions in e-commerce; nonetheless, there is still a lack of search interest from the user and online shopping intentions. To boost the user's search for product recommendations, use a search engine for the quest but not for purchasing purposes. Existing frameworks have some significant problems in the recommendation technology, such as new product problems, fewer evaluation problems, a vast amount of data, etc. Many e-commerce applications lack a better search experience like an Exploratory search system. This research work aims to create a conceptual model for recommendation systems using exploratory search, to study the behavior of users and the efficacy of exploratory search in terms of the quality of the search results produced. Several machine learning algorithms are used in this research to classify e-commerce products and evaluate the performance of these algorithms. The full-text search mechanism is used to implement an exploratory search system. The exploratory search system is evaluated by three criteria called to look up, learn and investigate. After the experiments and evaluation, it is observed that AdaBoost over decision tree performs better than the other classification algorithms implemented. The exploratory search system satisfies the three requirements that are lookup learn and investigate during the search process. Contributions to this work are to build a conceptual model for the recommendation system through the dataset of user events and finding the implementation of an exploratory search mechanism in an e-commerce application. � 2020 Mohammed Mahmudur Rahman, Zulkifly Mohd Zaki, Najwa Hayaati Binti Mohd Alwi and Md. Monirul Islam.