Interestingness recommendations in a computing advice facility
The present disclosure provides a recommendation to a user through a computer-based advice facility, comprising collecting topical information, wherein the collected topical information includes an interestingness aspect; filtering the collected topical information based on the interestingness aspect; determining an interestingness rating from the collected topical information, wherein the determining is through the computer-based advice facility; and providing a user with the recommendation related to the topical information based on the interestingness rating.
1. A computer-implemented method comprising:
detecting an acceleration in social activity associated with a content item posted on a website;
determining, based on the detected acceleration in social activity, that the content item is relevant to a particular user; and
providing, using one or more hardware processors, a recommendation of the content item to the particular user, based on the determining that the content item is relevant to the particular user.
2. The method of claim 1 , wherein the website is associated with an online social network.
3. The method of claim 1 , wherein the social activity is posted by a second user that is a connected to the particular user on an online social network.
4. The method of claim 1 , wherein the social activity is posted by a second user determined to be an influential user of an online social network.
5. The method of claim 1 , wherein the social activity is posted by a second user determined to have similar tastes to the particular user.
6. The method of claim 1 , wherein the social activity is saving a recommendation for the content item.
7. The method of claim 1 , wherein the social activity is at least one of commenting and discussing the content item.
8. The method of claim 1 , wherein the social activity is a rating of the content item.
9. The method of claim 1 , wherein the social activity describes a purchase of at least one of a product and a service.
10. The method of claim 1 , wherein the content item describes a location, and the social activity posted on the website is a check-in at the location.
11. The method of claim 1 , wherein the particular content item is a recommendation for at least one of a product and a service.
12. The method of claim 1 , wherein the particular content item is an advertisement.
13. The method of claim 1 , wherein the particular content item is a review of a product or service.
14. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:
detecting an acceleration in social activity associated with a content item posted on a website;
determining, based on the detected acceleration in social activity, that the content item is relevant to a particular user; and
providing a recommendation of the content item to the particular user, based on the determining that the content item is relevant to the particular user.
15. The storage medium of claim 14 , wherein the social activity is saving a recommendation for the content item.
16. The storage medium of claim 14 , wherein the social activity is at least one of commenting and discussing the content item.
17. The storage medium of claim 14 , wherein the content item describes a location, and the social activity posted on the website is a check-in at the location.
18. The storage medium of claim 14 , wherein the particular content item is a recommendation for at least one of a product and a service.
19. The storage medium of claim 14 , wherein the particular content item is an advertisement.
20. A system comprising:
a recommendation module, comprising one or more hardware processors, configured to:
detect an acceleration in social activity associated with a content item posted on a website;
determine, based on the detected acceleration in social activity, that the content item is relevant to a particular user; and
provide a recommendation of the content item to the particular user, based on the determining that the content item is relevant to the particular user.