Sequential RecSys — Your Interactions Matter
A dive into systems that try to perfect your action
In this article, we will see what sequential recommendations are compared to traditional methods. The first part will focus on the idea behind sequential recommenders, and the second will be an overview of the models and architectures used to perform the recommendations.
Recommender systems (RecSys) represent a fundamental domain in machine learning, versatile in their application across various use cases and data types. Essentially, a recommender system aims to predict and suggest items or content that users might be interested in, based on their past behaviors or preferences. These systems play a crucial role in enhancing user experience, engagement, and personalization across numerous online platforms and services.
Day by day, the presence of recommender systems shapes our online experiences in ways we may not even notice. From e-commerce giants like Amazon and entertainment platforms like Netflix to music streaming services such as Spotify, these behind-the-scenes algorithms are quietly at work, curating personalized content for each user. By meticulously analyzing vast troves of historical data, encompassing past purchases, searches, and user interactions, recommender systems possess the ability to make intelligent predictions…
