I built the one at Theneeds.com and, if you're interested, this is the one at Pinterest [1]. At Theneeds we were recommending news = fresh content based on use

I built the one at Theneeds.com and, if you're interested, this is the one at Pinterest [1]. At Theneeds we were recommending news = fresh content based on user's interest and other features. Because the content is fresh, you can't easily have enough data for a proper collaborative system. Our algo was essentially the Reddit algo, where a piece of content gets a rank based on time and log of score. Score in Reddit is the upvotes - downvotes. At Theneeds we had a more complex score including social signals (likes on fb / RT on tw) so we could compute a meaningful score also without a big community of users. The other difference wrt Reddit was having different scores and different paces (multipliers) based on categories of content, so for example news in tech and politics from newspapers were updating faster than news on travel from magazines. And by normalizing the ranks, you can merge multiple categories in one -- a feature that I think Reddit also added. As for the code/stack, custom written in python. We were using Redis to cache user timelines using sorted sets (including the guest users, i.e. the default top news for each category). In Redis, you can merge sorted sets, and we used it as an efficient way to create the new timeline when a new user was signing up. [1] https://medium.com/@Pinterest_Engineering/introducing-pixie-an-advanced-graph-based-recommendation-system-e7b4229b664b https://medium.com/@Pinterest_Engineering/introducing-pixie-... Edit: added more details about tech.