Recommender System with Machine Learning and Artificial Intelligence

20TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System

Madhusree Kuanr¹*, Puspanjali Mohapatra¹ and Sasmita Subhadarsinee Choudhury²

¹ Department of Computer Science, IIIT, Bhubaneswar, India

² MCKV Institute of Engineering, Howrah, India

Abstract

In the advancement of world wide web, people are getting more closer to online applications for fulfilling their most of the requirements. In the meantime, they have minimal free time to spend in the process of selection. Therefore, the need to take assistance of the recommender systems to resolve this issue is growing and recommender systems successfully providing users with personalized recommendations on various items to make the task of the users easier. This study aims at developing a recommender system based on tree data structure for farmers. The proposed system (TSARS) recommends seeds, fertilizers, pesticides and instruments based on farming and farmers’ location preferences when buying seeds online. It uses tree data structure to store the information of the database users. To find similar users for the active query user, a preorder traversal is used. The performance of TSARS is measured by different parameters like precision, accuracy and positive predictive value (PPV). The results revealed that better recommendation is provided by TSARS compared to traditional recommender systems.

Keywords: Recommendation system, collaborative filtering, tree data structure, precision, accuracy and positive predictive ...