Dimensionality reduction on slope one predictor in the food recommender system

Slope One Predictor is one of the most successful approaches for predicting the online rating-base collaborative filtering. The researcher examined the use of dimensionality reduction to improve performance for a new data set analysis in the process Slope One prediction which is used for analyzing data related to persons´ likes or interests in the menu of food that people do not want to eat similar dishes iteratively. This paper presents a method for extracting the user´s relationally similar behavior by searching for best neighbors in computing deviations between varied pairs of items or deviation matrix used this matrix to make predictions. The goals of improving accuracy of recommender systems that the researchers consider the menu fit for the data; therefore, finding the best technique and using the recommended data as needed by the inquirer is essential and vital in the future.