The Future of Recommendation Systems Using Clickstream Data and Embedding Technology

In the digital age, there is a surge in data generated from websites and online platforms. Clickstream data, which shows the paths users take within a website, is a crucial resource that allows us to infer user behavior and preferences. Efforts to provide personalized services are particularly active in e-commerce and content delivery platforms. Such data plays an essential role in offering personalized experiences to users.

Recommendation systems utilize clickstream data to provide personalized recommendations, thereby maximizing user experience and business outcomes. For instance, online shopping malls recommend products of interest to increase conversion rates, while streaming services suggest new content based on viewing history. This enhances user satisfaction and helps companies secure customer loyalty.

To effectively process large volumes of clickstream data, embedding technology is necessary. Embeddings convert data into low-dimensional vector spaces, making it easier for computers to understand. This technology reduces data complexity and facilitates similarity analysis.

This article covers the concepts of clickstream data and embedding technology, methodologies for building recommendation systems, real-world application examples, limitations…