Master of Science / Department of Computing and Information Sciences / Doina Caragea / In recent years, the content volume and number of users of the Web have increased dramatically. This large amount of data has caused an information overload problem, which hinders the ability of a user to find the relevant data at the right time. Therefore, the primary task of recommendation systems is to analyze data in order to offer users suggestions for similar data. Recommendations which use the core content are known as content-based recommendation or content filtering, and recommendations which utilize directly the user feedback are known as collaborative filtering. This thesis presents the design, implementation, testing, and evaluation of a recommender system within the recipe domain, where various approaches for producing recommendations are utilized. More specifically, this thesis discusses approaches derived from basic recommendation algorithms, but customized to take advantage of specific data available in the {\it recipe} domain. The proposed approaches for recommending recipes make use of recipe ingredients and reviews. We first build ingredient vectors for both recipes and users (based on recipes they have rated highly), and recommend new recipes to users based on the similarity between user and recipe ingredient vectors. Similarly, we build recipe and user vectors based on recipe review text, and recommend new recipes based on the similarity between user and recipe review vectors. At last, we study a hybrid approach, where both ingredients and reviews are used together. Our proposed approaches are tested over an existing dataset crawled from recipes.com. Experimental results show that the recipe ingredients are more informative than the review text for making recommendations. Furthermore, when using ingredients and reviews together, the results are better than using just the reviews, but worse than using just the ingredients, suggesting that to make use of reviews, the review vocabulary needs better filtering.