The investigation related to the influence of dimension manipulation on regret performance based on the upper confidence bound algorithm

Abstract

This paper aims to investigate the effects of dimension manipulation on the performance of a recommendation algorithm applied to a dataset of restaurant reviews. In this paper, the Zomato dataset which contains restaurant reviews and other relevant information in Bangalore City was used. This paper extracted ratings that each user gave for each restaurant from the list of user feedback for each restaurant. These different ratings were stored as a core feature that was used for the restaurant recommendation algorithm to estimate the true mean rating of each restaurant. Upper Confidence Bound bandit algorithm was used as the restaurant recommendation algorithm to find the restaurant with the highest average rating in the dataset. Dimension raising and dimension reduction were used as ways to manipulate dimension in this paper. Principal Component Analysis was used as the technique to reduce feature dimension in the dataset. It reduced features into two principal components and the first principal component was used in place of the original core feature. Dimension raising was implemented based on the original core feature and another feature that correlated with it the most. The product of these two features was used in place of the original core feature. The experimental results suggest that dimension manipulation leads to decreased regret performance when employing the Upper Confidence Bound algorithm for recommendation. Intriguingly, within the realm of dimension manipulation, dimension reduction exhibited a more adverse impact on regret performance compared to dimension raising.