Recommendations based upon explicit user similarity
A system and method for providing recommendations to individuals on a social network, in which the recommendations include information indicating the similarity of the individuals to one another, to aid the individuals in judging the degree to which the opinions of the others are applicable to the themselves.
1. A system, wherein the system comprises:
one or more processors configured to:
characterize a first user and a second user according to interaction information for the first user and the second user;
selectively scale attributes of the first user and the second user to correct for bias;
calculate a similarity level between the first user and the second user by determining a similarity score according to the scaled attributes of the first user and the second user; and
provide, to the first user:
a recommendation of a product or a service;
a list of categories and their respective similarity levels as related to the product or the service;
a calculation of one or more of the similarity levels; and
an interactive interface configured to display similarity levels in real time, wherein:
the interactive interface is configured to update dynamically according to new interaction data retrieved from a host system server associated with a user profile, and
the host system server is configured to utilize cloud-based infrastructure to integrate the user profile with one or more of a social network, e-commerce platform, and customer reward system.
2. The system according to claim 1 , wherein the first user and the second user interact with an e-commerce system.
3. The system according to claim 1 , wherein the interaction information is according to online social network activities of the first user and the second user via a network.
4. The system according to claim 1 , wherein the interaction information is according to implicit interactions.
5. The system according to claim 1 , wherein a level of similarity of the first user and the second user is generated according to an aggregated similarity level according to user interests and product preferences.
6. The system according to claim 1 , wherein the recommendation provided to the first user are according to a level of similarity of a first attribute weight vector and a second attribute weight vector.
7. The system according to claim 1 , wherein the interaction information is according to one or more of a vote on an online survey, an addition of an item to an online shopping cart, a purchase of one or more items, and an addition of an item to a catalog.
8. The system according to claim 1 , wherein a level of similarity of the first user and the second user is generated according to an aggregated similarity level according to user interests and product preferences.
9. The system according to claim 1 , wherein a level of similarity of the first user and the second user is generated according to the aggregated similarity level using answers to online surveys.
10. The system according to claim 1 , wherein the first user and the second user are characterized according to attributes.
11. The system according to claim 10 , wherein a level of similarity of the first user and the second user is generated according to an adjustment of a plurality of weights for each of the first user and the second user based upon a number of distinct users associated with each attribute.
12. The system according to claim 1 , wherein the list of categories and their respective similarity levels are used to determine whether opinions of the second user are useful to the first user.
13. The system according to claim 1 , wherein the one or more processors are configured to normalize the similarity levels for corresponding categories into different level categories.
14. The system according to claim 13 , wherein the normalized similarity levels comprise two or more different level categories.
15. The system according to claim 13 , wherein the normalized similarity levels comprise three or more different level categories.
16. The system according to claim 1 , wherein a privacy aspect of the second user is maintained.
17. The system according to claim 16 , wherein particular purchases of the second user are not revealed.
18. The system according to claim 1 , wherein the one or more processors are configured to:
express a strength of a relationship between the first user and each of a plurality of attributes as a plurality of weights; and
express a strength of a relationship between the second user and each of a plurality of attributes as a plurality of weights.
19. The system according to claim 18 , wherein the one or more processors are configured to calculate a metric of similarity of the first user and the second user according to the weights of the first user and the weights of the second user.
20. The system according to claim 19 , wherein the one or more processors are configured to express a graphical indication representing the similarity metric.
21. The system according to claim 1 , wherein the one or more processors are configured to selectively scale attributes of the first user and the second user, according to an Inverse User Frequency (IUF) factor, to correct for bias.
22. The system according to claim 1 , wherein the similarity score is generated according to a cosine similarity.