Towards Keyword Based Recommendation System

Recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analyzing such large-scale data. Moreover, most of existing recommender systems present the same ratings and rankings of items to different users without considering diverse users' preferences, and therefore fails to meet users personalized requirements. This project proposes a Keyword based Recommendation method, to address the above challenges. It aims at presenting a personalized recommendation list and recommending the most appropriate items to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, it is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Proposed system is used to improve the accuracy and scalability of service recommender systems over existing approaches.