Designing Hybrid Intelligence based Recommendation Algorithms: An Experience through Machine Learning Metaphor

Arup Roy

doi:10.31449/inf.v44i3.2926

Abstract

This article presents a summarization of the doctoral thesis which proposes efficient hybrid intelligent algorithms in recommendation systems. Development of effective recommendation algorithms for ensuring quality recommendation in timely manner is a tricky task. Moreover, traditional recommendation system is inadequate to cope up with the new technological trends. In order to overcome these issues, a batch of sophisticated recommendation systems has been discovered e.g. contextual recommendation, group recommendation, and social recommendation. The research work, investigates and analyzes new genres of recommenders using nature inspired algorithms, evolutionary algorithms, swarm intelligence algorithms, and machine learning techniques. The algorithms resolve some crucial problems of these recommenders. As a result, more precise personalized recommendation is ensured.

Highlights

Recommendation from known sources assist to achieve unknown tasks, e.g. purchasing of products, making plans for vacation, etc
In (a) Crowd-Sourcing based Group Recommendation Framework: The well-known Movie-Lens dataset is used in the experimentation purpose
In (b) Trusted Contextual Recommendation Framework: The Irish Trip-Advisor dataset is used in the experimentation

Summary

Introduction

Recommendation from known sources assist to achieve unknown tasks, e.g. purchasing of products, making plans for vacation, etc. Verbal assurance often lacks real-time information and consequences contradicting opinions. Users are overwhelmed by the voluminous information, and the possibility of opting wrong products could increase. Recommendation System (RS) becomes functional in such situations, e.g. movie recommendation of movielens.org, music recommendation of last.fm, product recommendation of amazon.com [1][2]. An RS lessens, the “information overload” problem as well as provide quick personalized recommendations [3]. The recommendation process consists of collecting user preferences, tracking the relevant data, and executing the recommendation algorithms [4]

Methods

Results

Conclusion