- You can build an effective recommender system with as little as two people.
- As you have more users, you tend to have more training data. Hence, you may have more accurate recommendations.
- More accurate recommendations may not be important to your users.
- The exact count of your users may not matter as much as the diversity of your users.
- A good rule of thumb is that you should have many more users than you have items to recommend.
- Given the right algorithms, your accuracy will improve monotonically with the number of users and the amount of training data.
- The users may enter feedback data to correct the assumptions of your recommender system and thus, improve it over time.
Explanation: The title of my blog post is the subject of an email I got recently. A very popular question.
Acknowledgment: Andre inspired me to write this post.