Tourism is a significant source of income for countries and regions, and with the advancement of technology, everything is now interconnected, generating massive amounts of data. Recommender systems are one way to utilize this generated big data. However, currently, Ethiopian Tourism Institutions do not have a system to manage tourist sites, analyze customer preferences and behavior, or filter based on their interests. Therefore, the purpose of this research is to develop a collaborative-based recommender system for Ethiopian tourism sites. This study applies both user-based collaborative filtering using a cosine similarity algorithm and model-based collaborative filtering using singular value decomposition (SVD), nonnegative matrix factorization(NMF), and K-nearest neighbors (KNNBasic) to analyze the data collected from the Amhara tourism office, which describes the Ethiopian tourism site. After the analysis, the results show that cosine similarity has the lowest root mean square error (RMSE) score. The methodology followed in this research is the design science approach, and the artifact recommender system is developed using the flask framework. After demonstrating the recommender system, domain experts evaluate it, achieving a promising result of 84.2%. The scarcity of data is a significant challenge, and thus, limited attributes for clustering are selected. Using more attributes to make the similarity of tourists will improve the development of the tourist site recommender system, which is left for future researchers to consider.