The benefits of Big Data Recommendation Systems on Different Platforms

Wang et al. (2020) provide a detailed analysis of the role of big data in the recommendation systems used in modern platforms including Facebook, YouTube, TikTok, and for retail sites. The focus of the article is on the composition of big data in data mining, data sharing, and data analysis. The large amounts of data are leveraged for financial gain and a better user experience for the end user. When applied to massive volumes of data, big data technology may be used for data mining, data analysis, and data sharing, among other things. By leveraging the value of data, big data technology has resulted in tremendous economic advantages for the companies that employ it (Wang et al., 2020). Additional benefits include the ability to assist in the formulation of social and economic policies. Information’s a new service economic model that treats data as a resource and then draws it from a range of different databases and other data sources to provide services to customers. This article is the most relevant I could find in relation to the study topic. This is because of how it relates components of big data with recommendation systems and the financial gain and better user experience that is expected.

A personalized shopping experience was always seen to be a luxury, but now businesses in a variety of industries are attempting to make their clients feel as if they are being treated like royalty at all times. With the help of big data, businesses now know their consumers better (Wang et al., 2020). As a result, understanding the idea and impact of recommendation systems is a must. Based on data analysis, a recommendation system recommends products, services, and information to customers. Recommendation systems, or recommendation engines, are data filtering tools that use machine learning algorithms and artificial intelligence to give the most relevant product to a specific client at the correct time. It will be easier to construct a recommendation engine if a corporation has a large amount of data. As a result, it may give suggestions for increasing revenue and consumer pleasure by giving relevant offers and a personalized purchasing experience.

In the advanced business world, competitiveness has led to innovativeness in every area of life. Wang et al., (2020) provide that big data serves to intensify that competition as people are becoming more predictable and recommendation systems are becoming more accurate by the day. For example, YouTube can now almost accurately predict what a person is interested in watching, including the tailored ads that are played mid-content. In recent years, big data technology has permeated every aspect of people’s lives and is now being used in a variety of industries, including healthcare and financial services, in the banking sector, on the internet marketplace, in the catering industry, in finance, in medical care, in the energy industry, for sports personnel, and in entertainment. Data collection technologies such as wireless sensor networks and big data processing techniques, for example, may one day be used to enable real-world applications such as driverless cars, new computer architectures, indoor localization systems, and road anomaly detection system to name a few examples.

The contribution of Wang et al. (2020) to the latest body of knowledge on big data and recommendation systems will pave the way for the next generation of research. It validates what is already public knowledge on the roles and benefits of big data, and how it will continue to change both the business and the personal user experience scene. Its heavy usage on social media platforms such as YouTube and Facebook will revolutionize how companies reach their target markets. The target industry, majorly platforms that are used for social networking and advertising, will benefit greatly in the near future.

References

Wang, J., Yang, Y., Wang, T., Sherratt, R. S., & Zhang, J. (2020). Big data service architecture: a

survey. Journal of Internet Technology, 21(2), 393-405.