The Influence of Food Bloggers toward Consumer's Attitude in Restaurant Selection: A Multi-objective Metaheuristic Approach

Abstract An analytical approach for a restaurant recommendation system that uses a food blogger ratings and the cost of food items in the restaurant is proposed by maximizing the restaurant rating and minimizing the overall cost. We construct a multi-objective optimization problem to get restaurant recommendations appropriate for the customer's budget and desired rating. Three evolutionary optimization algorithms, namely Nondominated Sorting Genetic algorithm II (NSGA II), Strength Pareto Evolutionary Algorithm 2 (SPEA 2), and Indicator-Based Evolution Algorithm (IBEA) have been used to identify approximated Pareto solutions for our proposed model. The effectiveness of the algorithms under consideration is given and evaluated against several performance indicators. Using Zomato restaurant data, we compare the results in terms of convergence and diversity, which confirms our suggestion for standardization. The suggested work contributes to an analytical approach based on evolutionary algorithm solutions to create a multi-objective restaurant recommendation system, which goes beyond the scope of previous works. Finally, we present a comparative result analysis with the existing Zomato rating using statistical tools and discuss the results.