From the course: Machine Learning and AI Foundations: Recommendations
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How to handle first-time users
From the course: Machine Learning and AI Foundations: Recommendations
How to handle first-time users
- [Narrator] Recommendation systems work great when the user's already entered lots of reviews, but for first time users we don't know enough about the user yet to make personalized recommendations. There are three ways we can try to work around this problem. First, we could just not make any recommendations for new users. For some applications it might be okay to wait until the user's reviewed products before making recommendations. A second approach is to use product similarity to suggest similar products to users who haven't rated anything instead of making personalized recommendations. But a third option is to use the average rating of products to make recommendations. In other words, we'll just recommend the products that have the best over-all ratings to new users. This can be helpful because some movies are just generally considered better than other movies. If a movie has a 5 star average rating across all users, that's probably a better movie to recommend to a brand new user…
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