The Impact of Algorithmic Product Recommendation on Consumers' Impulse Purchase Intention

Mingge Song

doi:10.54097/fbem.v11i3.13197

Authors

Mingge Song

DOI:

https://doi.org/10.54097/fbem.v11i3.13197

Keywords:

Algorithm product recommendation, Accuracy, Perceived practical value.

Abstract

With the development of the Internet and e-commerce, more and more information is presented in the public eye. Due to the limited human experience and time, the efficiency of processing massive amounts of information often decreases significantly. Algorithm based product recommendation is a recommendation system based on data mining and machine learning technology. It recommends products that users may be interested in by analyzing their historical behavior, personal preferences, and other factors. Algorithm product recommendation is generated to solve the problem of consumers facing difficulty in selecting a large number of products. An e-commerce website needs to recommend products that users may be interested in, but due to the large number of products, it is difficult to meet the personalized needs of each user. By using algorithmic product recommendation technology, e-commerce websites can recommend products that users may be interested in based on their historical behavior and personal preferences. This not only improves the shopping experience of users, but also increases the sales volume of e-commerce websites.

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