2016RePEc: Research Papers in EconomicsOpen access

Recommender systems in e-commerce applications

Gyöngyvér Kovács

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Abstract

One of the major problem with online shopping is finingd the right product, because finding the right product presumes that we know its name, but in most cases it is not so. For this reason the users need help in the process of online searching/shopping. Recommender systems have became a popular technique and strategy for helping users to select desirable products or services. In the past few years the recommender systems have changed from novelties used by a few big e-commerce sites, to serious business tools that are re-shaping the world of e-commerce. In this paper, we provide a brief overview of the classification of recommendation systems based on technology used to create recommendations, and inputs they need from the customers. Furthermore we analyze a few algorithms used by recommender systems and we will also present some marketing recommender systems and their comparative analysis.

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One of the major problem with online shopping is finingd the right product, because finding the right product presumes that we know its name, but in most cases it is not so. For this reason the users need help in the process of online searching/shopping. Recommender systems have became a popular technique and strategy for helping users to select desirable products or services. In the past few years the recommender systems have changed from novelties used by a few big e-commerce sites, to serious business tools that are re-shaping the world of e-commerce. In this paper, we provide a brief overview of the classification of recommendation systems based on technology used to create recommendations, and inputs they need from the customers. Furthermore we analyze a few algorithms used by recommender systems and we will also present some marketing recommender systems and their comparative analysis.

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Available abstract

One of the major problem with online shopping is finingd the right product, because finding the right product presumes that we know its name, but in most cases it is not so. For this reason the users need help in the process of online searching/shopping. Recommender systems have became a popular technique and strategy for helping users to select desirable products or services. In the past few years the recommender systems have changed from novelties used by a few big e-commerce sites, to serious business tools that are re-shaping the world of e-commerce. In this paper, we provide a brief overview of the classification of recommendation systems based on technology used to create recommendations, and inputs they need from the customers. Furthermore we analyze a few algorithms used by recommender systems and we will also present some marketing recommender systems and their comparative analysis.

Key concepts: Recommender system, Computer science, E-commerce, Product (mathematics), Process (computing), World Wide Web, Mathematics, Geometry

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