Collaborative Filtering Recommender Systems

Mehrbakhsh Nilashi,Karamollah Bagherifard,Nazanin Roozegar,Othman Ibrahim,Hamid Alizadeh,Lasisi Ayodele Nojeem

doi:10.19026/rjaset.5.4644

Mehrbakhsh Nilashi, Karamollah Bagherifard + Show 4 more

https://doi.org/10.19026/rjaset.5.4644

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Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision- making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.

Highlights

The history of recommender systems dates back to the year 1979 with relation to cognitive science (Rich, 1979)
In the mid-1990s, recommender systems became active in the research domain when the focus was shifted to recommendation problems by researchers that explicitly rely on user rating structure and emerged as an independent research area (Anand and Mobasher, 2005; McSherry and Mironov, 2009; Goldberg et al, 1992)
In the aftermath of user making a request, articulated depending on the recommendation approach by the user’s context and need, there exist a generation of recommendations aided by the use of various types of knowledge and data about the users, the available items and previous transactions stored in customized databases

Summary

INTRODUCTION

The history of recommender systems dates back to the year 1979 with relation to cognitive science (Rich, 1979). Recommender systems gained prominence among other application areas such as approximation theory (Powell, 1981), information retrieval (Salton, 1989), forecasting theories (Armstrong, 2001), management science (Murthi and Sarkar, 2003) and consumer choice modeling in marketing (Lilien et al, 2003). In coping with information overload problems, recommender systems have proved in recent years to be a force to reckon with as a valuable means in tackling such problems. In addressing this phenomenon, recommender system guides user towards new, unknown experienced items that may be relevant to the user’s current task. It is hoped that this research will accentuate the importance of recommender systems and provide researchers with insight and future direction on recommender systems

DATA AND KNOWLEDGE SOURCES

Based on functional knowledge KB approaches are

COLLABORATIVE FILTERING METHODS

EVALUATING COLLABORATIVE FILTERING ACCURACY

Actual rating

CONCLUSION

Conference on Uncertainty in Artificial