This week I continued working on adding more recipes into our recommendation system. Similar to the previous works, I am searching recipes online and process them by tagging and cleaning data into the designated format.
Another part of this week’s work is designing the test data set for the recommendation part. In general, I had made two ways to test if the recommendation system is working correctly; the first part is a set of requests list as the required format, and this set of requests will be used to manually check if the recommended recipes are correct(only uses the ingredients detected, and satisfy special requirements). Another part of testing will be offering some invalid requests, like missing ingredients, malformed requests, etc, and make sure that the system will not crash and can handle the issues.
In the past weeks, we once tested the image recognition API with the dataset we built. However, we didn’t take the accuracy but only observed that the API works on our test set. Therefore, this week I also started rerunning the API on the dataset in order to get the numeric accuracy. In addition, we have a set of photos we took with real ingredients, which will also be applied to test the API in different situations.
Currently, I am on the schedule; the recommendation part only needs the progress in the number of recipes. For next week’s plan, we will be working on the final presentation, and I will put the most effort into the presentation as the presenter.