New system cold start: Recommender systems

Question

I have built a recommender systems which has tens of thousands of items and their feature descriptions, but no user profiles as of now. I am looking for pointers to approaches that can help me bootstrap the system, so I can do some evaluation. I would appreciate any pointers to papers/applications that have addressed this problem.

Answer 1

How to deal with the cold-start problem depends a lot on your specific application.

An easy way of dealing with the user cold-start problem is to present the new user with random items, or the most popular items, or hand-selected items, and start learning from them.

Another way is to present users with a questionnaire, and then present items to them according to the results. Or you directly show them items/products and let them rate/select the ones they like.

Also note that in web-based system you usually know some things about your users: Which operating system/browser they use, where they (roughly) come from, which language they speak. All this information can be used.

Papers:

• see the Wikipedia article on the topic
• My answer to another question on StackOverflow lists some papers for dealing with new items - most of the methods would also be applicable to new users.
• Another approach is to select products/items that will help you most for learning about the user. Just out of my head, you can find them by querying Google Scholar for "recommendation" and the terms "decision trees", "active learning", "user cold-start", and so on.