Couple of ideas for you - Consider injecting information with "oracles" An oracle is a kind of virtual user that likes one thing and only one thing. For exam

Couple of ideas for you - Consider injecting information with "oracles" An oracle is a kind of virtual user that likes one thing and only one thing. For example they only watch movies that have been tagged sci-fi. This sci-fi oracle adds information about sci-fi-ness to your data which is useful for several things. It helps with the cold start problem as new items can be automatically tagged by the appropriate oracles and get past the zero information horizon quickly. Also you can measure a users sci-fi affinity by measuring that users similarity to the sc-fi oracle. - Another way to think about co-occurrences is as connected nodes in a digraph. You have users and items and connections between them (user watched video). Start with an item and traverse all the links to the other side (all the users who watched this video) then for each user traverse to the items side (you can roll up the occurrences for a score) and you have similar items. Works equally as well for finding similar users. - Create an "average user" and use that as a seed for new users. If we know nothing else we should expect a new user to be close to average. This means they will probably get recommended the most popular items but - Find items with divisive scores or groups and ask new users their opinion on those items to find out about them. After a new user gets created consider asking them their opinion on five of these divisive items. Their ratings should swiftly put them in an informed space the way taking five steps down a binary tree does a lot to reduce search space. - I like the way you use simple plus one smoothing for your scores. I'm not sure why this doesn't get used more often. Good luck with the project!