Facilitating Like-Minded User Pooling
Some embodiments can provide a user matching system configured to match a list of one or more users to a given user. The user matching system can be configured to employ a stage learning process including a user compatibility learning stage, an affinity learning stage, and a match optimization stage. In various exemplary implementations, various user data regarding user preferences, user traits, user behaviors, and/or any other user aspects can be collected. In those implementations, the user matching system is configured to divide the users into different user groups based on the learned user attributes, and determine similarities among users within a given group based on the user attributes. In this way, one or more users can be identified and can be suggested to the given user based on their similarities to the given user.
1 . A method for pooling users, the method being implemented by a processor configured to execute computer program components, the method comprising:
receiving information regarding users;
dividing the users into user groups using the received information based on a first set of one or more user attributes, the user groups including a first user group;
for each user group:
determine similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes;
for a first user in the first user group:
determine one or more users similar to the first user such that the each of the one or more users has a similarity score with respect to the first user that is above a threshold similarity score; and
generating a recommendation for the first user based on the one or more users similar to the first user.
2 . The method of claim 1 , wherein the first set of one or more user attributes include one or more personal factors regarding the users, one or more investment factors regarding the users, and/or one or more web viewing factors regarding the users.
3 . The method of claim 1 , wherein dividing the users into user groups using the received information based on the first set of one or more user attributes comprises:
applying K-means clustering to the users based on the first set of one or more user attributes.
4 . The method of claim 1 , wherein determining the similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes comprises:
for the users in the first group:
constructing a first user matrix indicating Euclidean distances among the users with respect to the first set of one or more user attributes;
constructing a second user matrix indicating Euclidean distances among the users with respect to a second set of one or more user attributes; and
determining the similarity score among the users in the first group using the first and second user matrixes.
5 . The method of claim 4 , wherein determining the similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes further comprises:
for the users in the first group:
constructing a third user matrix indicating user viewing activities with respect to a first type of web items; and wherein, the determination of the similarity score among the users in the first group further uses the third user matrix.
6 . The method of claim 5 , wherein the first type of web items include webpages comprising information regarding real-estate properties.
7 . The method of claim 5 , wherein determining the similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes further comprises:
for the users in the first group:
constructing a fourth user matrix indicating user viewing activities with respect to a second type of web items; and wherein, the determination of the similarity score among the users in the first group further uses the fourth user matrix.
8 . The method of claim 5 , wherein the second type of web items include webpages comprising information regarding investment items including stocks, bonds, or mutual funds.
9 . The method of claim 1 , wherein the recommendation includes information recommending a web item that has been viewed by at least some of the one or more users similar to the first user.
10 . The method of claim 1 , wherein the recommendation includes information recommending the first user to form a user group with at least some of the one or more users similar to the first user.
11 . A system for pooling users, the system comprising a processor configured to execute computer program components such that when the computer program components are executed, the processor is caused to perform:
receiving information regarding users;
dividing the users into user groups using the received information based on a first set of one or more user attributes, the user groups including a first user group;
for each user group:
determine similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes;
for a first user in the first user group:
determine one or more users similar to the first user such that the each of the one or more users has a similarity score with respect to the first user that is above a threshold similarity score; and
generating a recommendation for the first user based on the one or more users similar to the first user.
12 . The system of claim 11 , wherein the first set of one or more user attributes include one or more personal factors regarding the users, one or more investment factors regarding the users, and/or one or more web viewing factors regarding the users.
13 . The system of claim 11 , wherein dividing the users into user groups using the received information based on the first set of one or more user attributes comprises:
applying K-means clustering to the users based on the first set of one or more user attributes.
14 . The system of claim 11 , wherein determining the similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes comprises:
for the users in the first group:
constructing a first user matrix indicating Euclidean distances among the users with respect to the first set of one or more user attributes;
constructing a second user matrix indicating Euclidean distances among the users with respect to a second set of one or more user attributes; and
determining the similarity score among the users in the first group using the first and second user matrixes.
15 . The system of claim 14 , wherein determining the similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes further comprises:
for the users in the first group:
constructing a third user matrix indicating user viewing activities with respect to a first type of web items; and wherein, the determination of the similarity score among the users in the first group further uses the third user matrix.
16 . The system of claim 15 , wherein the first type of web items include webpages comprising information regarding real-estate properties.
17 . The system of claim 15 , wherein determining the similarity scores among the users in the group based on multiple user attributes including the first set of one or more user attributes further comprises:
for the users in the first group:
constructing a fourth user matrix indicating user viewing activities with respect to a second type of web items; and wherein, the determination of the similarity score among the users in the first group further uses the fourth user matrix.
18 . The system of claim 15 , wherein the second type of web items include webpages comprising information regarding investment items including stocks, bonds, or mutual funds.
19 . The system of claim 11 , wherein the recommendation includes information recommending a web item that has been viewed by at least some of the one or more users similar to the first user.
20 . The system of claim 11 , wherein the recommendation includes information recommending the first user to form a user group with at least some of the one or more users similar to the first user.