Recommendation System Based on Item and User Similarity on Restaurants Directory Online

The growing number of internet companies are demanding the company to innovate through technology. This is also applied to restaurant directory companies, they should give recommendation of restaurant which suit best on customer needs. This study aims to develop a system to provide recommendation for customer in restaurant selection. We merge the item similarity and user similarity features to generate recommendations. Evaluation shows that the recommendation system based on item similarity yields higher F1-measure value when comparing to user similarity.