A Comparative Analysis of Genetic Programming and Genetic Algorithm on Multi-Criteria Recommender Systems
Shweta Gupta, Vibhor Kant · 2020
Recommender systems (RSs) are software tools that work as guides by suggesting products to users from a vast catalogue of products. Various approaches and techniques have been developed to provide effective recommendations to users. Classical collaborative filtering (CF) based RSs helps users by providing suggestions based on their overall assessment of items. However, providing suggestions based on their overall assessment is not an efficient way. So, multi-criteria recommender systems (MCRS) came into existence as an extended approach for suggesting products to users based on multiple features of products, and adding these multiple features can enhance the performance of the system. However, aggregation of these feature assessment i.e. feedback provided to multiple criteria is a key issue in MCRS. In this paper, we present a comparative analysis of genetic algorithm (GA) and genetic programming (GP) approaches to aggregate criteria ratings for predicting user preferences in MCRS. These two algorithms are bio-inspired and have great potential to solve optimization problems. In this research, GP and GA are used to solve the aggregation problem in MCRS by estimating weights for each criterion in a system. We compared the results of genetic programming and genetic algorithm approaches to show their effectiveness in multi-criteria rating systems.