Recommendation system begin very popular in recent years and are adopted in many fields, such as movies, musics, and E-commerce items. We choose food items for our recommendation items. The biggest challenge of food recommendation is the sparsity of any food-item set under considerations. Therefore, we propose Clustering items for better food recommendation as ourthesis. Clustering items for better food recommendation takes users’ dietary records as the input and then predicts food which users most likely to eat next day. In this thesis, we propose an iterative cluster method (ITC) which cannot only solve the sparsity problem but also derive more meaningful clustering for food recommendation. The proposed ITC includes model prediction and reclustering method. For model prediction, we adopt LSTNET[4] to predict clusters based on users’ past dietary records. There clustering method reclusters food items based on the prediction of clusters and the groundtruth. The above two stages are repeated until the results are saturated. Our proposed method was evaluated over users’dietary records dataset, which is from MyFitnessPal[1]append are provided by Singapore Management University. It was shown that our method achieved the map[11] score of 0.323.