Patent Application
20090319914
On-line communities (e.g., marketplaces, social networking sites, etc.) generally provide a reputation system through which participants may be assessed against some criteria. For example, on-line marketplaces may allow participants to rate each other's qualities as buyers and sellers (e.g., how fast a buyer pays, how accurately a seller describes an item to be sold, etc.). Gaming communities may provide ratings of a participant's skill as a gamer. Business networking sites may summarize a person's qualifications to participate in various types of businesses.
Information about participants could be made available in an unfiltered form, or could be distilled in some manner. For example, an on-line marketplace might display the unfiltered ratings or narrative reviews that a participant has received. As an alternative to displaying this unfiltered data, information could be distilled from the data. For example, if there are hundreds of numerical ratings of a participant, an average rating could be displayed instead of displaying all of the individual ratings.
While on-line communities may collect data of general interest (e.g., “John pays for his purchases on time”) or basic facts that people might have in common (e.g. “Susan and Fred went to the same college”), these communities generally have not used the data to discover, describe, or foster relationship dynamics between participants. Reputation systems in on-line communities generally focus on “me” not “us”. For example, in a marketplace, reputation systems generally focus on whether a participant has certain qualities that make him or her a good buyer or seller, not on that participant's relationship with other participants.
Some on-line communities may also allow participants to self-identify their relationship with others in the community: e.g., social networking sites may allow participants to identify each other as “friends.” However, such self-assessment systems rely on the participants' self-assessment or self-identification of their relationship with others.
Data about participants in an on-line community or marketplace may be analyzed in order to discover relationship dynamics between participants. The relationship dynamics may then be used to drive an interactive experience between participants. The interactive experience may be analogous to a one-to-one reputation system, in which those facets of one participant's on-line persona may be highlighted to another participant, based on the participants' expressed or inferred interests. The existence and/or extent of common interests, behaviors, acquaintances, etc., could be used to “rate” a relationship (e.g., based on the historical or potential depth of that relationship), and this rating could be expressed in some manner such as through a visual metaphor. For example, a tree in various stages of growth could be displayed in order to express the extent of an existing relationship between participants, or to express the potential for a relationship between participants.
For example, participant A may have an interest in the band “U2”, and this interest may have been expressly stated in participant A's profile, or may be inferred from participant A's activities (e.g., purchasing tickets to a U2 concert on-line). If participant B is found to have an interest in the same band, then this fact could highlighted to one or both participants while they interact in an on-line setting. Moreover, facts such as common interests (e.g., bands, hobbies, etc.), common activities (e.g., sharing photos on line, gaming, etc.), the amount of interaction between people (e.g., whether two people chat with each other once a day, once a week, etc.), or any other facts, could be used as a basis to assess the level of relationship between these people. The level of such a relationship could be communicated using a user interface element, such as a graphic representing the tree metaphor mentioned above.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
On-line communities typically offer some type of reputation system. For example, on-line marketplaces allow participants to rate each other's qualities as buyers or sellers. Other types of on-line communities may display ratings of participants based on some criteria—e.g., a participant's level of technical expertise in some area, a participant's skills as a gamer, etc. These reputation systems typically focus on a particular participant, rather than on a relationship between participants.
Some communities allow participants to self-define (or mutually-define) relationships with other participants. For example, participants might identify each other as “friends,” and might even self-characterize the “strength” of the friendship. However, these systems generally do not attempt to identify aspects of a relationship from basic or organic facts collected about participants. The subject matter described herein may be used to assess a relationship between participants. Commonalities between participants may be identified based on facts such as the participants' self-provided profiles, usage patterns, or information extracted from conversations, blogs, or other types of content. Commonalities could be identified to the participants as part of the user interface (UI) that the participants use to interact with the community. Moreover, an assessment of a relationship could result in some sort of “rating” of the relationship. While it may sound somewhat clinical to speak of “rating” one person's relationship with another person, such a rating could benefit participants by helping them to determine which members of a community may be of greatest interest to them. The rating could be provided in the form of a visual metaphor, such as a tree in various stages of growth, representing the depth or extent of the existing or potential relationship between participants (although an assessment of a relationship could be presented in any manner).
Turning now to the drawings,
In system 100, several participants interact with each other in an on-line community 102. On-line community 102 may be a social networking community, a marketplace, or any other type of community. Participants 104 participate in on-line community 102.
Each of participants 104 may participate in on-line community 102 through a machine, such as a desktop computer, handheld computer, wireless telephone, or any other kind of machine that allows participants to interact with each other. In the example of
Interaction engine 118 may facilitate interaction among participants. Interaction engine may connect to the participants in some manner (as shown by connections 107, 109, and 111 with between interaction engine 118 and participants 106, 108, and 110, respectively.) Interaction engine 118 could take any form that is appropriate for the particular on-line community 102, and for the manner in which participants 104 interact with each other. For example, if on-line community 102 is implemented as a web site, then interaction engine 118 may comprise a plurality of servers that implement various functions of the web site, and/or software that uses those computers to implement front-end and/or back-end functionality of the web site. The server(s) that implement social networking web sites, on-line marketplaces, etc., are examples of interaction engine 118. Such an interaction engine 118 could perform actions such as providing the web-browser-based interfaces to implement sale/purchase transactions, chat rooms, photo sharing, or any other function. In addition to the web-browser-based interaction described above, on-line community 102 might be implemented through some mechanism other than web browsers. For example, if on-line community 102 is organized around video games, then interaction engine 118 could be one or more servers that interact with gaming clients. Interaction engine 118 could take any form, and could perform any type of action that facilitates interaction among participants 104.
Interaction engine 118 may provide participants 104 with various types of information about relationships among participants. To this end, interaction engine 118 may use various components. For example interaction engine 118 may comprise, or otherwise make use of, relationship database 120, which stores information about relationships between participants 104. For example, the nature of a relationship between two participants (e.g., the relationship's depth, strength, etc.) may be rated on some type of scale, and information about this rating may be stored in relationship database 120. Commonalities between participants also could be stored in relationship database 120. In one example (which is described below in connection with
In order to assess relationships among participants, interaction engine 118 may comprise, or otherwise make use of, analyzer 122. Analyzer 122 takes organic information about participants and identifies aspects of the relationship between pairs of participants 104. Analyzer 122 may store (as indicated by 123) data about these relationship aspects into relationship database 120. Analyzer 122 could identify aspects of a relationship “off-line” by using otherwise idle time to analyze facts about participants 104, or it could analyze relationships among participants “on-demand” (e.g., at the time that the specific participants, whose relationship is to be analyzed, are interacting with each other). The result of the analysis performed by analyzer 122 could take the form of a rating of the relationship between a pair of participants (e.g., the strength of relationship, or potential relationship, between two participants could be rated on a scale such as low-medium-high, or on a numerical scale, or some other type of scale). Or, the analysis could produce various types of specific information about the relationship between two participants (e.g., specific activities or interests that the participants have in common, etc.).
Analyzer 122 may use various tools to analyze the relationships between participants. For example, analyzer 122 may comprise, or otherwise make use of, a relationship metric 124. As mentioned above, part of the analysis that analyzer 122 may perform is to rate the relationship between participants. Relationship metric 124 may be used to make this rating. Relationship metric 124 could take facts into account in any appropriate way in order to arrive at a rating. For example, relationship metric 124 may take into account the number of interests or activities in common between two participants, the number of discernible differences between the participants, a comparison of the participants' own self-provided data (e.g., their ages, geographic locations, educational levels, etc.), or any other type of information.
Analyzer 122 may produce various results of its analysis of a relationship. For example, analyzer 122 may produce relationship rating 126. As another example, analyzer may produce an indication of commonalities 128 between two participants. Commonalities 128 could describe anything that two participants have in common, such as the fact that two participants are both interested in music or in a particular musical artist or performer (or in some other type of artist or performer), the fact that two participants like to share photos on-line, the fact that two participants went to the same school, or any other area in that two participants might have in common. (
Results produced by analyzer 122 may take various forms. For example, results could exist in the form of raw data to be stored in database 120. As another example, results could be presented as part of a user interface. For example, when two participants are interacting with each other, interaction engine 118 may present, to the participants, a user interface that incorporates aspects of the analysis of these participants' relationship. For example, the user interface could display the rating of the participants' relationship (e.g., using the tree metaphor described above, or using some other type of user interface element). As another example, if commonalities 128 between participants are detected, then these commonalities could be worked into the user interface presented by interaction engine 118. For example, if two participants are both interested in the band “U2”, then the user interface presented by interaction engine 118 could highlight this fact to the participants, and might even inform them of an upcoming concert and/or offer the participants the chance to buy tickets to the concert.
In order to present the results of an analysis to participants in the form of a user interface, interaction engine 118 may comprise, or otherwise make use of, a user interface (UI) generator 130. For example, if relationship rating 126 is to be presented in the form of the tree metaphor mentioned above, then UI generator 130 could generate the tree graphics that constitute a representation of the relationship. Additionally, UI generator 130 could generate UI elements showing commonalities, or other features of a relationship between participants.
User interface 200, in the example of
In the example of
As mentioned above, a relationship may be rated in some manner. An analyzer may have determined a rating that applies to an existing or potential relationship between the participants “Steve” and “April”. This rating may be displayed, as part of user interface 200, in the form of visual element 208. In the example of
As a result of the analysis of the relationship between “Steve” and “April,” it may be determined that these two participants have certain commonalities. For example, it may be determined that both participants like the band “U2”. This fact could be determined based on purchases the participants have made, conversations in which they have mentioned the band, self-identification of interests, or from any other data. Thus, some type of information based on the commonality could be provided as part of user interface 200. The information provided could take any form. For example, a message 210 could be rendered informing the participants that they both like “U2”. As another example, a suggestion 212 for one participant to invite another to an upcoming U2 concert could be rendered. The suggestion could be in the form of a link, which, when activated, might offer the opportunity to buy tickets to the concert. (Message 210 and suggestion 212 could be presented in the form of pop-up balloon 214, as shown in
While
At 302, one or more connections from one or more participants are received. For example, if interaction engine 118 (shown in
At 303, source data about the participants may be extracted. As discussed below in connection with
At 304, the relationship between participants is analyzed. The analysis may occur when the participants interact with each other. Or, the analysis could be performed at some time before the participants interact with each other, so that an analysis of their relationship is ready “on the shelf” in the event that the participants begin to interact with each other. The analysis may generate a rating of the relationship between the participants, a set of commonalities, or any other type of information about the relationship.
At 306, UI elements about the relationship are rendered. The UI elements could include an indication of the relationship rating (block 308) (e.g., the tree metaphor mentioned above, a numerical rating, a star rating, etc.). The UI elements could take any form such as text, images, audio, etc. The UI elements could also include information about commonalities (block 310), or any other information about the relationship (block 312) that was gleaned in the analysis of the relationship.
As noted above in connection with
One example source of facts about a participant is conversations 402. Data mining and extraction techniques may be used to analyze a participants' on-line conversations with other participants in order to determine a person's interests, activities, likes, dislikes, etc. (A participant might agree to have his or her conversations analyzed in this manner, and, in consideration of privacy concerns that many people have, might be offered the opportunity to opt-out of such analysis.)
Another source of facts about a participant is self-description 404. When a participant registers to be part of an on-line community, the participant may provide various information about himself or herself, such as his or her name, geographic location, age, interests, education, or any other type of information. This information could be used as a basis to analyze relationships among participants, such as the commonalities that exist between two people.
Another source of facts about a participant is activities 406 in which the participant engages. For example, some on-line communities may offer participants the opportunity to engage in certain activities, such as photo sharing, music purchases, blogging, or other activities. The fact that a person participates in these activities may be a basis to analyze a relationship. For example, the fact that two participants like to share photos on-line is an activity that these two participants have in common, and this commonality may be part of their relationship analysis.
Yet another source of facts about a participant is blogs 408. Blogs can be analyzed in a manner similar to conversations, using data mining and extraction techniques. (Moreover, the fact that a person maintains a blog is an activity of that person, and if two people are both bloggers, then this fact may be a commonality of their relationship.)
The foregoing are some example sources of facts that could form the basis of a relationship analysis. However, the facts to be analyzed could come from any source.
While
One type of fact that may be used as part of a relationship analysis is how long a given participant has been a member of a community (block 502). People often join communities to try them out. Some people continue with the community while others do not. The fact that a person has been a member of an on-line community for some length of time may suggest that the person has a greater potential to form a relationship with other members of the community. Similarly, the amount that particular participants have actually interacted with each other in the past may be an indication of the existing or potential level of their relationship. For example, the number of times that participants have interacted, or the frequency of their interaction (e.g., daily, weekly, monthly, etc.) could be used to determine the existing or potential level of a relationship.
Another fact that may be used as part of a relationship analysis is whether participants share photos (block 504). In a social networking site, some participants communicate by sharing photos while others do not. The fact that two participants have shown an interest in communicating in this manner may suggest that these two participants have the potential to form a relationship with each other in the community. Similarly, the fact that two participants communicate using the same medium, such as e-mail, instant messaging, etc., may suggest that the two participants have the potential for a relationship within the community (block 506). The fact that two participants communicate using mobile devices (block 508), such as handheld computers or messaging-enabled wireless telephones, may suggest a potential for a relationship, since people who use these devices tend to communicate at a different frequency, and/or with different types of content, than people who communicate mainly with desktop or laptop computers.
Other facts that may be used in a relationship analysis are whether two participants both have an interest in music (block 510) or video games (block 512). The particular choice of musical artists or games may also be considered in a relationship analysis—e.g., the fact that two participants like the same artist or the same game may give them something in common, and thus might increase the relationship rating and/or be considered a commonality between two participants.
Computer 600 includes one or more processors 602 and one or more data remembrance components 604. Processor(s) 602 are typically microprocessors, such as those found in a personal desktop or laptop computer, a server, a handheld computer, or another kind of computing device. Data remembrance component(s) 604 are components that are capable of storing data for either the short or long term. Examples of data remembrance component(s) 604 include hard disks, removable disks (including optical and magnetic disks), volatile and non-volatile random-access memory (RAM), read-only memory (ROM), flash memory, magnetic tape, etc. Data remembrance component(s) are examples of computer-readable storage media. Computer 600 may comprise, or be associated with, display 612, which may be a cathode ray tube (CRT) monitor, a liquid crystal display (LCD) monitor, or any other type of monitor.
Software may be stored in the data remembrance component(s) 604, and may execute on the one or more processor(s) 602. An example of such software is relationship analysis and/or interaction software 606, which may implement some or all of the functionality described above in connection with
The subject matter described herein can be implemented as software that is stored in one or more of the data remembrance component(s) 604 and that executes on one or more of the processor(s) 602. As another example, the subject matter can be implemented as software having instructions to perform one or more acts of a method, where the instructions are stored on one or more computer-readable storage media. The instructions to perform the acts could be stored on one medium, or could be spread out across plural media, so that the instructions might appear collectively on the one or more computer-readable storage media, regardless of whether all of the instructions happen to be on the same medium.
In one example environment, computer 600 may be communicatively connected to one or more other devices through network 608. Computer 610, which may be similar in structure to computer 600, is an example of a device that can be connected to computer 600, although other types of devices may also be so connected.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
