Survey and evaluation of food recommendation systems and techniques
Akshi Kumar, Pulkit Tanwar, Saurabh Nigam · International Conference on Computing for Sustainable Global Development · 2016
Recommending proper food is of paramount importance for person's sound health. It is also useful in revenue generation in restaurants by recommending varied choices of food or recommending restaurants depending on the type of food to customers. In this paper, we survey the existing food recommendation engines and compare the different recommendation algorithms — Content based filtering, collaborative filtering, hybrid techniques etc. Further, we would see the challenges and limitations of each technique.