AdaBoosting for case-based recommendation system

Abstract

Recommender systems are ways for web personalization and crafting the browsing experience to the users' specific needs and are tools for communicating with large and complicated information spaces. It give a personalized view of these spaces, ranking items likely to be of interest to the user. Now-a-days many on-line e-commerce applications like Amazon.com, Netflix etc. use personalized recommendations. Recommender systems research has integrated a wide range of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. The purpose of this paper is to show how recommendations can be generated for case-based scenarios using AdaBoost machine learning algorithm. The technique has been used to predict the restaurants a user may like based on the data gathered from past.