In 2021, it has been nearly ten years since the Hundred Regiments War on the Internet. With the continuous advancement of time, technological development is also changing with each passing day. From the perspective of e-commerce, the traditional "people looking for goods" has become "goods looking for people"; from the perspective of content, Namibia Phone Number from the active acquisition of information, to passively receiving and mining effective information from massive information. And these all rely on recommender systems. For users, it acts as a "means of information filtering", which can maximize the efficiency of users in the current situation of Namibia Phone Number information overload, and establish users' trust and deep links to products. For products and companies, it can greatly improve user activity and retention, and improve user conversion rates, which will bring huge commercial value to the product.
I remember that Amazon (Amazon) was the first to apply the recommendation system to the industry. It was only in 2003 that it applied the traditional collaborative filtering algorithm to the field of "people and goods matching", opening up a new path for e-commerce. Today's recommendation systems have become more AI-based and intelligent through continuous Namibia Phone Number changes in technology. The development of machine learning and deep learning continues to empower it, making "recommendation" a standard feature of today's products. We have entered the era of AI, Namibia Phone Number and the boundaries of products and technologies will become more and more blurred in the future. At a certain level, the formulation of effective strategies often depends on the understanding of technology. This article focuses on the two modules of recall and ranking in the recommendation model.
Overall framework First of all, if you want to ensure the recommendation accuracy of the entire recommendation system, you need to rely on a large amount of basic sample data. Under offline conditions, a large amount of sample data is "feed" to the Namibia Phone Number recommendation model, and the recommendation model will fit a set of universality. The algorithm formula, so that when each user comes to the product, it can be brought into the algorithm Namibia Phone Number formula based on user data, and a result can be obtained at the output layer of the model. This result is reliable, and it is recommended to users. As users continue to give feedback on the results given by the recommendation system, the characteristics of users will become more and more abundant, and the recommendation accuracy will also become higher and higher. , creating a virtuous circle.
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