Keyword Analysis & Research: cold start problem machine learning


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What is it like to face cold-start problems in machine learning?

The experiences for data scientists who face cold-start problems in machine learning can be very similar to those, especially the excitement when our models begin moving forward with increasing performance. We will itemize several examples at the end.

What is the cold start problem in recommender systems?

As part of my machine learning internship at Wish, I’m tackling a common problem in recommender systems called the “cold start problem”. Cold start happens when new users or new items arrive in e-commerce platforms.

What is the cold start problem?

Synonymous with the failure of a car engine to turn when the weather is cold, the cold start problem confounds recommenders across two categories – product and user. When a new product or service hits the market, it has no likes or dislikes, reviews, or clicks, thus making it difficult to recommend.

What is cold start in AI?

But at the start line, i.e. cold start, since the user (or the product) is new there’s no track record to fall back on. In today’s world of commercial artificial intelligence (AI), the cold start is also where the solution can now include a degree of automated data modeling.


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