Abstract

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.

Keywords:

Collaborative filtering, item-based, prediction, rating, recommender system, user-based, recommendation,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.