Bytedance Daily Intern Recruitment – Recommendation system architecture

What the recommendation system probably does is pick out the articles that interest you the most from a bunch of them. For example, the recommendation on the home page of each app and guess what you like are all related to the recommendation system. The challenges of the whole system are certainly considerable. For example, each user of the article, together is very, very much, so how to quickly screen out the articles you are interested in that batch; For example, the “like” function in the article, every user is “like”, how to efficiently and accurately deal with these “like” requests; For example, each article has an ID, and the ID itself is the feature, so the total number of features is quite large. How to save these features and update the corresponding weight of these features? For example, the recommended articles should not be too monotonous, not always on the same topic, how to explore your unknown interests. And as the business changes and new ideas are proposed, there are always new challenges. Anyway, do something challenging with us. The teachers and sisters here are very enthusiastic and will help you smooth through the adaptation period, so that you eventually grow into a professional recommended engineer.