Comparison of User Based and Item Based Collaborative Filtering in Restaurant Recommendation System

Abstract

In today's rapidly evolving digital landscape, the success of the restaurant industry hinges not only on culinary excellence but also on the ability to deliver personalized and memorable dining experiences.To achieve this, recommendation systems have emerged as indispensable tools, with Collaborative Filtering standing out as a promising method.This study aims to assess the effectiveness of Item-Based and User-Based methodologies within the context of a restaurant recommendation system.The research methodology involves loading and manipulating data within a matrix framework, followed by normalization.Both Item-Based and User-Based approaches are then applied to the normalized matrix, using Pearson Correlation and Cosine Similarity as comparative metrics.Through a comprehensive evaluation, the study identifies the User-Based technique as superior, demonstrating a Pearson Correlation coefficient of 0.012391 and yielding the lowest Mean Absolute Error (MAE) value.Furthermore, analysis using Spearman correlation data reveals significant correlations within the User-Based approach algorithm, with a notable proportion falling within specific ranges.Specifically, 50% of correlations fall within the ranges of 0.96-0.99 and 0.96-0.97.These findings underscore the effectiveness of the User-Based approach in refining the precision and reliability of restaurant recommendations, particularly when compared to the Item-Based approach.In conclusion, this research sheds light on the efficacy of different recommendation methodologies within the restaurant industry's digital landscape.The findings have implications for enhancing personalized dining experiences and improving the overall customer satisfaction within the industry.