Larry Page, founder of Google, said: "Our goal is to get you the right information at just the right time … without you having to ask first." This is Arbital's goal as well. While there is a basic subscription system in place already, most of the features described below are not yet implemented.
Whether or not information is right for any given user depends on how relevant it is to their interests. The simplest way for people to express their interest is to subscribe to topics or users they find interesting. To take a step further, the user should be able to specify, if they want, how strong their interest is, or that, in fact, they are not interesting in anything in that area. For example, on Facebook you can say that you want to see some users' posts first. But you can't say that you don't want any posts about politics. You are either subscribed to everything a user posts or none of it, and there is no support for topics.
On Arbital, you will be able to subscribe to any particular topic, ranging from as broad as Math or as narrow as Roboto Font, and to any user. You can vary the strength of the subscription, including saying that you are extremely disinterested in a given topic. Arbital will aggregate your interests and use them to predict whether any given piece of information is relevant to you. For example, let's say there is a recent, popular blog post discussing a new cryptocurrency, Stellar. A few people you follow liked it and/or commented on it. Even though you are not subscribed to that blog, as long as you are subscribed to a few relevant topics e.g. Bitcoin, Arbital will still show the post in your feed. It'll be able to use all the signals: post's popularity, friend's likes and comments, and your related subscriptions, to predict that you'll likely enjoy reading this post.
You might have seen that Arbital already has a functional, instant, fuzzy search. Moving forward, we will make searching Arbital easier and more powerful. The search will be able to leverage all the metadata that Arbital has about the page's content and how the page is connected to everything else. For example, you'd be able to search for "all papers at the intersection of 'Vitamin D' and 'Cancer' topics, which also have p<0.05."
To be able to execute a search query like the one above, the system needs to have the structured data about papers and other objects. A common complaint about Wikipedia is that while it has a lot of data, most of it is not structured, which makes it very hard to use in computation. On Arbital, we plan to address this problem early on.
%%comment: ### Arbital: intraweb solution Many companies have an internal wiki system because it works well for linking, searching, and categorizing information. (Much better than, say, Google Docs.) Arbital is [1sl better than any wiki], and it has [17h private subdomains] that give any group their own private instance of Arbital. (You can still leverage Arbital's public content.) If you are interested in using Arbital for your company, send us an email: email@example.com %%