A Proposed Business Intelligent Framework for Recommender Systems

Sitalakshmi Venkatraman

doi:10.3390/informatics4040040

Abstract

In this Internet age, recommender systems (RS) have become popular, offering new opportunities and challenges to the business world. With a continuous increase in global competition, e-businesses, information portals, social networks and more, websites are required to become more user-centric and rely on the presence and role of RS in assisting users in better decision making. However, with continuous changes in user interests and consumer behavior patterns that are influenced by easy access to vast information and social factors, raising the quality of recommendations has become a challenge for recommender systems. There is a pressing need for exploring hybrid models of the five main types of RS, namely collaborative, demographic, utility, content and knowledge based approaches along with advancements in Big Data (BD) to become more context-aware of the technology and social changes and to behave intelligently. There is a gap in literature with a research focus in this direction. This paper takes a step to address this by exploring a new paradigm of applying business intelligence (BI) concepts to RS for intelligently responding to user changes and business complexities. A BI based framework adopting a hybrid methodology for RS is proposed with a focus on enhancing the RS performance. Such a business intelligent recommender system (BIRS) can adopt On-line Analytical Processing (OLAP) tools and performance monitoring metrics using data mining techniques of BI to enhance its own learning, user profiling and predictive models for making a more useful set of personalised recommendations to its users. The application of the proposed framework to a B2C e-commerce case example is presented.

Highlights

Recommender systems (RS) have been used to assist users in finding the intended items more effectively over the Internet and have been developed predominantly for enhancing users’ search results
The main difference between this work and others reported in literature previously is that the proposed methodology is more generic and the framework with model steps detailed here could be applied to any business situation, while previous studies are for specific situations such as proposing a recommender system for a security solution or business-business e-commerce application [5,6]
A significant amount of research explored the five main types of recommender techniques. As they exhibit complementary advantages and disadvantages, hybrid models were proposed for specific industry scenarios

Summary

Introduction

Recommender systems (RS) have been used to assist users in finding the intended items more effectively over the Internet and have been developed predominantly for enhancing users’ search results. Modern e-businesses would like to have intelligent and context-aware information filtering techniques adopted for its active users to find the best suitable items of interest with diverse content, such as movies, audio, books, and documents for improving quality of experience in a variety of fields [7]. It is of high importance in the design of RS to relate user perceived quality of experience.

Comparison of RS Types

Methodology for the Proposed BIRS Framework

Use Case of BIRS framework

Useorder

Browser

Conclusions and Future Work