Abstract

The demand of recommender systems has been greatly increased for the last few times after the trending of personalization within various kind of systems. This leads to the better achievement of the best recommendation or more precise result could be for both service providers or users. So there is actually a need for users to create their own custom recommendation in a small scale. That would help them get personal recommendations as simple and fast as possible, but without having to involved in a complex system. For the better and more precise result, the system would be enhanced with semantic similarity method. One of the best method to achieve this is by using a framwork, such as Crab Python framework for building recommender engines or systems. As the outcome, regular users could provide themselves with simple custom recommendations by their own configuration. The best case would present users the most related recommendations with high accuracy of preferences.