INTEREST-BASED COMMUNITIES

1 . (canceled)

2 . A computer-implemented method comprising:

accessing community content of an online community in a networked system, the online community including a group of users of the networked system with a similar interest, the community content related to the similar interest and available according to the networked system;

generating a graphical user interface that displays the community content according to a first display order of a plurality of postings of the community content;

identifying one or more key terms that are representative of the group of users with the similar interest;

accessing the community content including the plurality of postings;

determining a recency of each posting in the plurality of postings;

accessing reputational information of a corresponding poster of each posting in the plurality of postings;

determining relevancy of each posting in the plurality of postings to the online community based on inclusion of the one or more key terms in each posting;

performing, using at least one processor, a weighted average calculation of each posting in the plurality of postings based on the recency, reputational information, and relevancy of each posting;

determining a second display order of the plurality of postings in the community content based on the weighted average calculation; and

updating the community content in the networked system based on the second display order; and

changing, in response to and based on updating the community content, the graphical user interface to display the community content according to the second display order.

3 . The computer-implemented method of claim 2 , further comprising:

selecting at least one activity threshold; and

notifying a user not among the group of users of the networked system of the existence of the online community when the user has the similar interest and the at least one activity threshold is met.

4 . The computer-implemented method of claim 3 , wherein selecting at least one activity threshold comprises selecting at least one of:

a purchasing threshold, the purchasing threshold based on a frequency of occurrence of at least one of a number of purchases using the networked system, a volume of purchases using the networked system, or a total dollar amount of purchases using the networked system with one or more terms relating to the community content;

a browsing threshold, the browsing threshold based on a frequency of occurrence of at least one of when the user searches an item within the networked system, or when the user purchases an item from a first category but searches for an item in a corresponding category; and

a sales threshold, the sales threshold based on a frequency of occurrence of at least one of a sale of an identified item type, a number of sales of the identified item type, or sales from the user totaling a predetermined dollar amount.

5 . The computer-implemented method of claim 2 , further comprising:

identifying a micro-community, the micro-community including users interested in a specific topic selected from among topics of interest to the group of users of the networked system with the similar interest; and

establishing the micro-community from within the online community when a predetermined threshold size for the micro-community is met.

6 . The computer-implemented method of claim 5 , wherein identifying the micro-community comprises:

identifying the micro-community based on a frequency of occurrence of at least one micro-community factor selected from a group of micro-community factors including search terms used within the networked system, search terms plus view item patterns within the networked system, search terms plus view items divided by bid patterns within the networked system, favorite sellers within the networked system, the users buying from a common favorite seller within the networked system, and locality of the users within the networked system.

7 . The computer-implemented method of claim 2 , wherein:

the plurality of postings includes at least one of a listing of items for sale at a fixed-price sale, a listing of items for sale by auction, a posting of a blog, and a posting of a message board.

8 . A computer-implemented method comprising:

identifying, using at least one processor, a community within a networked system, the community including a group of users of the networked system with a similar interest and each user of the group of users being a member of the community, the identifying of the community comprising performing, using a category hierarchy, a text/relationship analysis on transaction data to identify at least one of a key term and a phrase for the community that is representative of the group of users with the similar interest, the transaction data including a transaction history between users involving at least one product offered for sale;

assessing system activity of a potential candidate to the community, the potential candidate selected from among all users of the networked system;

determining that the potential candidate has met a purchasing threshold based on the user activity, the purchasing threshold being based on a number of purchases made by the potential candidate using the networked system; and

in response to determining the potential candidate has met the purchasing threshold, inviting the potential candidate to join the community as an initial member.

9 . The computer-implemented method of claim 8 , wherein identifying at least one of the key term or the phrase for the community that is representative of the group of users with the similar interest comprises:

parsing data from at least one of transaction data or event data to identify at least one of a key term or a phrase representative of the group of users within the networked system with the similar interest, the transaction data including information regarding transactions in the networked system, the event data including information regarding user activity within the networked system.

10 . The computer-implemented method of claim 8 , wherein identifying at least one of the key term and the phrase for the community that is representative of the group of users with the similar interest further comprises:

identifying at least one of a key term and a phrase representative of the group of users within the networked system with the similar interest, the category hierarchy including a number of categories of different item types available through the networked system.

11 . The computer-implemented method of claim 10 , wherein performing the text/relationship analysis on a transaction history comprises:

creating a cluster of keywords from the category hierarchy; and

assessing relationships among the cluster of keywords to select one or more keywords from among the cluster of keywords as at least one of the key term or the phrase representative of the group of users within the networked system with the similar interest, the relationships including interactions between the users of the networked system that use one or more keywords from the cluster of keywords.

12 . The computer-implemented method of claim 11 , wherein creating the cluster of keywords from the category hierarchy includes selecting a cluster of keywords from more than one category of items of the category hierarchy.

13 . The computer-implemented method of claim 11 , wherein assessing relationships among the cluster of keywords comprises:

performing a tf (term frequency)*idf (inverse document frequency) analysis on the cluster of keywords to select from among the cluster of keywords the key term or the phrase representative of the group of users within the networked system with the similar interest.

14 . The computer-implemented method of claim 8 , wherein identifying at least one of the key term or the phrase for the community that is representative of the group of users with the similar interest comprises:

accessing a number of documents from at least one of transaction data or event data, the transaction data including information regarding transactions in the networked system, the event data including information regarding user activity within the networked system, each of the number of documents having title information, the title information including a title from each of the number of documents;

creating a suffix tree using the title information for the number of documents;

selecting a merge criterion; and

merging base clusters of the suffix tree based on the merge criterion to identify at least one of a key term or a phrase from among the number of documents that is representative of users with a similar interest.

15 . The computer-implemented method of claim 8 , wherein:

the system activity includes at least one of user activity within the networked system or relationships between the potential candidate and users of the networked system.

16 . The computer-implemented method of claim 8 , further comprising:

inviting the potential candidate to join the community as a moderator when a moderator threshold is met;

inviting the potential candidate to join the community as an administrator when an administrator threshold is met; or

inviting the potential candidate to join the community and providing an incentive for joining the community when a joining incentive threshold is met.

17 . A non-transitory machine-readable medium comprising instructions, which when executed by a machine, cause the machine to perform operations comprising:

accessing community content of an online community in a networked system, the online community including a group of users of the networked system with a similar interest, the community content related to the similar interest and available according to the networked system;

generating a graphical user interface that displays the community content according to a first display order of a plurality of postings of the community content;

identifying one or more key terms that are representative of the group of users with the similar interest;

accessing the community content including the plurality of postings;

determining a recency of each posting in the plurality of postings;

accessing reputational information of a corresponding poster of each posting in the plurality of postings;

determining relevancy of each posting in the plurality of postings to the online community based on inclusion of the one or more key terms in each posting;

performing, using at least one processor, a weighted average calculation of each posting in the plurality of postings based on the recency, reputational information, and relevancy of each posting;

determining a second display order of the plurality of postings in the community content based on the weighted average calculation;

updating the community content in the networked system based on the second display order; and

changing, in response to and based on updating the community content, the graphical user interface to display the community content according to the second display order.

18 . The non-transitory machine-readable medium of claim 17 , wherein the operations further comprise:

selecting at least one activity threshold; and

notifying a user not among the group of users of the networked system of the existence of the online community when the user has the similar interest and the at least one activity threshold is met.

19 . The non-transitory machine-readable medium of claim 17 , wherein the operations further comprise:

identifying a micro-community, the micro-community including users interested in a specific topic selected from among topics of interest to the group of users of the networked system with the similar interest; and

establishing the micro-community from within the online community when a predetermined threshold size for the micro-community is met.

20 . A non-transitory machine-readable medium comprising instructions, which when executed by a machine, cause the machine to perform operations comprising:

identifying a community within a networked system, the community including a group of users of the networked system with a similar interest and each user of the group of users being a member of the community, the identifying of the community comprising performing, using a category hierarchy, a text/relationship analysis on transaction data to identify at least one of a key term and a phrase for the community that is representative of the group of users with the similar interest, the transaction data including a transaction history between the users involving at least one product offered for sale;

assessing system activity of a potential candidate to the community, the potential candidate selected from among all users of the networked system;

determining that the potential candidate has met a sales threshold based on the user activity, the sales threshold being based on a number of sales made by the potential candidate using the networked system; and

in response to determining the potential candidate has met the sales threshold, inviting the potential candidate to join the community as an initial member.

21 . The non-transitory machine-readable medium of claim 20 , wherein identifying at least one of a key term and a phrase for the community comprises:

identifying at least one of a key term and a phrase representative of the group of users within the networked system with the similar interest, the category hierarchy including a number of categories of different item types available through the networked system.

22 . The non-transitory machine-readable medium of claim 21 , wherein performing the text/relationship analysis on a transaction history using a category hierarchy comprises:

creating a cluster of keywords from the category hierarchy; and

assessing relationships among the cluster of keywords to select one or more keywords from among the cluster of keywords as the key term or the phrase representative of users within the networked system with the similar interest, the relationships including interactions between the users of the networked system that use one or more keywords from the cluster of keywords.