CN105229684B

CN105229684B - System for controlling and optimizing information distribution between users in information exchange

Info

Publication number: CN105229684B
Application number: CN201480026012.XA
Authority: CN
Inventors: 布莱恩·麦克法登
Original assignee: Bu LaienMaikefadeng
Current assignee: Bu LaienMaikefadeng
Priority date: 2013-03-15
Filing date: 2014-03-16
Publication date: 2021-02-19
Anticipated expiration: 2034-03-16
Also published as: PH12015502098A1; EP2973293A2; ZA201507465B; CN112967068A; WO2014145365A3; WO2014145365A2; CN105229684A; SG11201507452PA; EP2973293A4; HK1214389A1

Abstract

An automated control system for regulating the exchange of information between an information producer and an information consumer. A control mechanism may dynamically refine decisions to include or exclude information items from a consumer's information stream to improve success metrics for similar participation. One or more system interface request control mechanisms may dynamically provide limits on audience goals, priorities, preferences, and other data for stimuli and inputs. The administrator may set parameters and select success metrics to balance the goals of the information exchange participants and the goals of the stakeholders. The system may also be used to resolve conflicts between the selection criteria of the consumer and the audience goals of the producer.

Description

System for controlling and optimizing information distribution between users in information exchange

Cross Reference to Related Applications

The present application claims the benefit of provisional patent application No. 61852280 filed on 2013, 3, 15 by Brian D McFadden.

Technical Field

The present invention relates to a system for controlling and optimizing information distribution between users in an information exchange.

Background

A major drawback in services such as social networks, user groups, list servers, forums, and question and answer services, where users or members exchange information, is the inability to more accurately and optimally regulate the flow of information between producers and consumers. Some practices have been in place to make these exchanges manageable and relevant to participants, but these practices lack the automatic dynamic refinement needed to potentially optimize or substantially improve the goals of stakeholders. Other services such as news syndication services, newspapers, magazines, media, advertising networks, blogs, research services, etc., where one group is exchanging information with another, face similar problems.

One of the partial solutions used by many information exchanges is to add groups, tags or topics that information consuming users can subscribe to or use to filter the set of information they can obtain. This is an improvement, but not a complete solution, because increasing the number of topics to reduce the information rate produced per topic is still inefficient, because the consuming user has to choose between a low rate of information flow and some valuable information items that may be lost from the peripheral topics. Once they subscribe to the peripheral topic, the information rate and value dilution increases. Even if the interests of an information consumer are contained within a single topic, there is still a certain degree of variability in interests that can lead to particular inefficiencies if there are many information items within a given topic.

Another problem with the theme-only approach is to have the information consuming user specify the theme selection. This is particularly problematic in view of the constantly changing and evolving ontology of topics. Techniques are often employed to obtain preferences or interests from previous action affordances (levels) of a user's activities. A wide variety of approaches are available (both public and proprietary) to identify items of interest based on past behaviors and interactions (e.g., clicking and viewing histories), collaborative filtering suggestions, machine learning, and others. These methods generate a set of preferences for the information consumer that may be conflicting or have varying ranges of applicability and accuracy of results. The uncertainty in deriving the preference will also vary. To accommodate these types of situations, preferences are often ranked and applied in rank order. This approach has the limitation of not considering the dynamic extrinsic factor, the status of information exchange, the producer of the information item and their target preference for the information item. These and other factors may have an impact on the applicability of the preferences of the information consumer (particularly when there is an expected uncertainty in the obtained preferences).

Another contributing problem is the practice of many information exchanges that reduce or eliminate any limitation of information items entered by information producers. This approach encourages volume but also results in variable contribution quality that ultimately reduces the value of the potential consumer in the interaction and underestimates the problems stated so far. This situation may not change significantly (even when there is a monetary assessment of the contribution). Although payment is a possible quality-related limitation, it is not guaranteed that a better quality level can be achieved.

While various methods are available to control the flow of information to the user and allow the user to self-regulate the flow of information, in many cases they are often suboptimal, non-dynamic, and inefficient.

Disclosure of Invention

In one exemplary embodiment, a method for using a computer system in information exchange for distributing information items from at least one information producer to at least one information consumer is provided, the method comprising the steps of: obtaining an audience target indicating a type of information consumer that the producer wants to receive the information item; obtaining selection criteria indicating whether the information consumer wants to receive the information item; adjusting the decision matrix using a decision control loop to improve or maintain the success metric; determining whether to deliver the information item to the information consumer based on the decision matrix.

Other variations:

in the above method, a plurality of audience targets are specified.

In the above method, a priority is assigned to the audience target.

In the above method, a plurality of selection criteria for the information consumer are used.

In the above method, a priority is assigned to the selection criterion.

In the above method, the audience target represents a continuous mapping of consumer user profile attributes to continuum or priority values.

In the above method, the selection criteria represent a sequential mapping of producer user profile attributes to sequential systems or priority values.

In the above method, the selection criterion represents a sequential mapping of information item attributes to continuum or priority values.

In the above method, the decision matrix resolves conflicts between the audience targets of the information producer and the selection criteria of the information consumer.

In the above method, the value of the success metric is participation of the information consumer.

In the above method, the decision matrix is a two-dimensional region, the two-dimensional region representing audience target priorities in one dimension and selection criteria priorities in another dimension.

In the above method, the audience target priority is between-1 and 1, and the selection criteria priority is between-1 and 1.

In the above method, the threshold limit divides the region into an inclusion region and an exclusion region.

In the above method, the threshold limit is dynamically derived from system internal and external metrics.

In one exemplary embodiment, a computer system interface for inputting audience targets is provided that includes an information producer limit control loop and that will: receiving at least one audience target from an information producer; calculating an audience size that the audience target can reach; using audience size limit mappings for correlating audience size and priority; determining an audience target priority from the audience size limit mapping; the audience targets are thus assigned priorities.

In the above system interface, the audience size limit maps to assign a lower priority to a larger audience size.

In the above system interface, the audience targets are ranked.

In the above system interface, a further cumulative size is calculated for each audience target having an inclusion behavior, and the cumulative size is a union of audiences with higher ranked audience targets having an inclusion behavior matching, and the audience size is used to determine an audience target priority for a specified audience target, wherein audience target behavior is included as the cumulative size.

In the above system interface, the cumulative size excludes intersections of audiences matching higher ranked audience targets, where the audience target behavior is excluded.

Brief Description of Drawings

Fig. 1 depicts an example of information exchange.

FIG. 2 depicts an example of producer interaction.

FIG. 3 depicts an example of general user interaction.

FIG. 4 depicts an example of consumer interaction.

Fig. 5 depicts an example of a basic decision matrix in which no behavior priority shows audience goals for the producer vertically down and horizontally.

Fig. 6 illustrates an exemplary embodiment of a decision matrix with behavior priority.

Fig. 7 illustrates an exemplary embodiment with sequential priority.

Fig. 8 includes regions and threshold lines for a decision grid or decision matrix.

FIG. 9 depicts audience population limits.

Fig. 10 depicts a system interface for inputting audience targets.

Detailed Description

An example of an information exchange 29 is shown in fig. 1. Users 20 of information exchange 29 may be information producers 22 or information consumers 28 or both. The information exchange 29 transfers information items 24 from the information producer 22 to the information consumer 28. In the most general definition, the information exchange consists of one or more producers, one or more consumers, and distributors 26. The distributor 26 specifies how information items flow from the producer to the consumer.

Distributor 26 may take a variety of forms including, for example, information switches that simply pass from distributor to consumer, sender to recipient, publish-subscribe, or any other form in which information is communicated from producer to consumer. Distributor 26 would include, for example, a situation where a consumer associates (friends) or follows one or more producers or joins a group, or where producers and consumers have agreed to follow or associate or exchange information with each other and allow other parties to do so as well. Distributor 26 may or may not support subscriptions. If the subscription is supported, then consumer 28 may be subscribed to one, several, or all producers. If distributor 26 does not support subscriptions, then consumer 28 will be able to receive from all producers. There may be one or more producers 22. There may be one or more consumers 28. The information exchange 29 may be a social network, a group within a social network, a listing server, a news syndication service, news delivery, newsletter, digest, bid, alert, advertisement exchange, advertising network, email client, news reader, web browser, web portal, or any service that facilitates the flow of information items from a producer to a consumer.

Producer 22 is a user 20 who will send, post, place, contribute, publish, compose, create, instruct, respond to, or otherwise cause information to be distributed to or viewable by one or more other users of the information exchange. Fig. 1 is not intended to show every detail of the information flow.

The information consumer 28 is the user who will receive the information item originating from the producer. Consumers 28 may or may not consume the information items that they may use.

It is noted that label producers and consumers are related to information production and information consumption and are by no means meant to be in a commercial relationship.

The informational items 24 may be messages, emails, notifications, responses, video clips, audio clips, news, articles, stories, inquiries, bids, advertisements, URLs, or any other form of communication that a producer or consumer may send or make available.

An information stream is an aggregation or collection of information items that are transmitted (directly or embedded) to consumers 28 in sequence or together via a medium including, but not limited to, printing, email, web delivery, mobile messaging, video, audio, broadcast, or via any other means of transmitting information.

In fig. 3, an example of an information exchange 29 is shown in which a user 20 may enter user profile data 64 into a system interface for entering a user profile 61. The system interface for entering a user profile 61 stores the user profile 60 in a user profile store 62. The user profile store may be an internal part of the information exchange, an external part of the information exchange, or a combination of internal and external. User profile data 63 acquired by a set of systems may also be stored in user profile store 62, and in some systems, the user may not enter any user profile data.

The user profile 60 includes (without limitation) available information about the user. This includes, but is not limited to, behavioral, biographical, demographic, historical, rating, feedback, tracking, or other general or specific information from sources both internal and external to the information exchange 29. The form of the user profile store includes a relational database, name-value pairs, no-sql, hierarchical data, objects, nested hierarchical data, or a combination of databases in a single source or multiple sources. If accessible via an API, the user profile 60 may be represented by XML, JSON, CVS, or any other data representation.

Consumer 28 in fig. 4 may enter selection criteria data 66 into a system interface for entering selection criteria 68, and selection criteria 65 are stored in selection criteria store 67. Selection criteria 65 may indicate the type or set of information items that the consumer is potentially interested in or not interested in receiving. The system interface for entering selection criteria 68 stores the stored selection criteria in selection criteria store 67. Selection criteria store 67 may be internal to information exchange 29, external to information exchange 29, or a combination of internal and external. The selection criteria may also include system-derived selection criteria 69, which may also be stored in selection criteria store 67. In one embodiment, the selection criteria may be stored with the user profile data, and the user profile storage and selection criteria storage may be the same.

In one embodiment, the selection criteria store and the user profile store may be stored together on a continuous store for quick access and processing.

In FIG. 2, audience target 50 defines a set of consumers or audiences that producer 22 is willing or unwilling to reach. The system interface for inputting audience targets 44 interacts with a producer limit control loop 46 and an audience target request control loop 48. The producer limit control loop 46 and the audience target request control loop 48 adjust the audience targets 50 that the information items 24 to be processed by the distribution subsystem 52 include.

In fig. 2, a system interface for entering information items 40 receives information items 24 from producer 22. The metadata request control loop 42 interacts with the system interface for entering information items 40 and adjusts the amount of additional descriptive data collected when entering information items 24. In fig. 2, a distribution subsystem 52 processes information items 24, audience targets 50, a set of metrics 54, a user profile from a user profile store 62, and selection criteria from a selection criteria store to determine what a consumer should obtain, receive, or view information items (as described below). The metrics 54 may be metrics, statistics, and parameters obtained directly or in a computational form from one or more sources internal or external to the information exchange.

In one embodiment, distribution subsystem 52 and distributors 26 may be the same. In another embodiment, they may be independent.

Description of the operation

In one embodiment, the system described herein is an information exchange or an integral part of an information exchange. In another embodiment, the system will exist independently of the exchange of information when the subsystem interacts with the exchange of information, as described in detail below.

In one embodiment, the system is computer code software running on a computer system. The computer systems may be any combination of one or more physical computer hardware systems, physical servers, appliances, mobile devices, CPUs, auxiliary CPUs, embedded processors, workstations, desktop computers, virtual appliances, virtual servers, virtual machines, or similar related hardware with an applicable operating system for the particular hardware, and in more than one case, interconnected via a private or public network.

In one embodiment, the system is operable as a self-regulating automated control system.

Producer of

In one embodiment, the producer may enter information item 24 into a system interface for entering information item 40. The information items are composed of content and meta-descriptions. Content may include a summary, a title, a full story, an image, video, audio, multimedia, or other primary information transfer object. The meta-description may include a summary, a source, keywords, authors, a signature, related links, subject matter, subject, type, restrictions, pricing, or any other field or object or hierarchical data used to classify, categorize, track, identify, or otherwise describe content and information items. In one embodiment, the metadata description and the information item may be the same.

In one embodiment, a producer may input audience targets into a system interface for inputting audience targets 44. Audience target describes consumers that a producer wants to reach or does not want to reach. The specification of audience targets may refer to any aspect of a user profile that specifies potential consumers. Audience targets will have behavior that specifies whether users matching the audience targets should receive information. In one embodiment, the system interface for entering information items and the system interface for entering audience targets may be the same.

In one embodiment, the producer may specify one or more additional audience targets that they want. The first audience target is the set of primary users that the consumer wants to include or exclude. Each additional audience may have a lower priority than the previously selected audience.

In one embodiment, producer 22 may construct audience targets and priorities by selecting one or more parameters from available data in a consumer's user profile and assign priorities to the values of each discrete parameter and the value ranges of the continuous parameters. The combined maximum and minimum of all field values may be used to determine the normalized priority range.

In one embodiment, the producer may first target the maximum audience they want to reach. The system may set a limit that is less than the audience size specified by the first audience target. In one embodiment, the limit may be determined by the context of the message, past history of interactions with previous messages from the producer, and current system-wide metrics. In another embodiment, the system may adjust the limit in the transaction paid or some other concession from the producer. In one embodiment, the producer may specify additional audience targets to reach an audience that is closer in size to the limit. If the size of the audience is less than the limit, the audience target may be used as a priority for input and distribution. In one embodiment, if the size of the audience is greater than the limit, the system will refine the goal to make the audience goal meet the limit or adjust the priority of the audience goal. In one embodiment, if the size exceeds a limit, the system may adjust the priority of the audience target. In one embodiment, the producer may scale the priority based on one or more discrete parameters or continuous parameters used in the consumer profile.

In one embodiment, a producer may have a profile of predefined audience targets that may be selected in lieu of entering and creating new audience targets.

In one embodiment, the information items and audience targets may be sent to a distribution subsystem. In one embodiment, the distribution subsystem may be integral with the information exchange distributor. In another embodiment, the distribution subsystem may be external to the information exchange distributor.

In one embodiment, an audience target for the producer may be required. In another embodiment, the audience target of the producer may be optional.

In one embodiment, the producer may use visual input sliders to indicate audience goals and priorities of specific profile attributes. For example, audience targets with higher priority are targeted based on years of experience of the consumer. In another embodiment, a producer may use drag-and-drop vision to rank and set audience target priorities.

In one embodiment, the audience target entered by the producer may apply to a single information item, multiple information items, or all information items from the producer.

In one embodiment, the producer may be an autonomous agent.

User' s

In one embodiment, users, producers, and consumers may enter data into user profiles 60. In another embodiment, user profile 60 may also include system data and information about the user including, but not limited to, performance, behavior, history, tracking, or any other information that the system may record or calculate for the user. In another embodiment, the user profile may also include external information obtained from external systems including, but not limited to, performance, behavior, history, tracking, logging, or any other information that may be obtained or calculated from external systems or combined with internal profile data. In another embodiment, the user profile may have data from all data sources.

The information exchange user 20 may enter user profile data 64 into the system interface for entering the user profile 61. The system interface for entering the user profile 61 stores user profile data 64 in the user profile store 62. In one embodiment, the user profile store may be part of the information exchange 29. In another embodiment, the user profile store 62 may be external to the information exchange 29. In another embodiment, user profile store 62 may be distributed between information exchange 29 and outside of information exchange 29. In one embodiment, external and system-derived user profile data 63 may be stored in user profile store 62.

Consumers

In one embodiment, a consumer may enter selection criteria defining an information item type and may also define a producer type. In another embodiment, the selection criteria may specify only the information item type or the producer type. In one embodiment, the consumer may enter an action to select criteria to specify whether the information items that match the criteria are items they want to receive or do not want to receive. In another embodiment, the behavior assigned to the selection criteria may be assigned by the system from the behavioral actions of the consumer. For example, by expressing consumers who are interested in related items or metadata topics.

The consumer may enter more than one selection criterion. In one embodiment, if more than one selection criteria is specified, the consumer may specify a priority that defines how important the criteria are. The priority may be represented by sorting the criteria or by selecting a priority preference input. In another embodiment, the priority of the selection criteria may be assigned by the system from the context or behavior, history of the entry or acquisition of the selection criteria, or the action that caused the selection criteria to be created.

Selection criteria may also overlap and conflict. For example, a conflict between criteria may arise if two selection criteria match and one criteria, say, includes a specified item and the other criteria, say, excludes the specified item. In one embodiment, conflicts may be resolved by preferring the highest priority selection criteria. In one embodiment, the priorities may be combined in a mathematical function to determine the priority, with-1 being optionally multiplied to exclude the priority. The function may take into account the higher weight of the higher priority or may simply average the priorities. If the two priorities are the same in a conflict, then it may be considered unresolved or open. In another embodiment, conflicts may be resolved by the system according to the optimization criteria discussed below.

In one embodiment, the selection criteria and priority of selection criteria may be determined from performance, history, behavior, or tracking data of the consumer. In another embodiment, the selection criteria and priority may be determined from predictive statistical methods. In another embodiment, the selection criteria entered by the consumer may be combined with selection criteria determined from all other means.

In one embodiment, the priority may be set by the system for each selection criterion. In another embodiment, the system sets a default priority for the selection criteria that can be changed by the consumer.

In one embodiment, the processing of consumer selection criteria may be integral to the information exchange distributor. In another embodiment, the processing may be external to the default distributor.

In one embodiment, the selection criteria for the consumer may be input by a person. In another embodiment, the selection criteria may be input by an autonomous agent.

The consumer may enter selection criteria into a system interface for entering selection criteria. The system interface for entering selection criteria 68 stores the selection criteria in selection criteria store 67. In one embodiment, the selection criteria store 67 may be part of an information exchange. In another embodiment, the selection criteria store 67 may be external to the exchange of information. In another embodiment, the selection criteria store 67 may be distributed between the information exchange and outside of the information exchange 29. In one embodiment, the system-derived selection criteria 69 may be stored in the selection criteria store 67.

In one embodiment, the consumer ranks and prioritizes selection criteria using drag-and-drop vision.

In one embodiment, the consumer may be an autonomous agent.

Decision matrix

In one embodiment, decision matrix 70 may be used to determine whether information items 24 should be included in the information flow of consumer 28.

Fig. 5 shows a decision matrix 70a for a basic case in which there is no audience target priority or selection criteria priority.

In fig. 5, the producer 22 behavior for two audience targets 50 is shown in the horizontal direction. Two audience targets 50 are used for send and not send behavior. The letter 'S' indicates a transmit action and the letter 'DS' indicates a no transmit action. The letter 'O' for openness indicates a case where no audience target is applied to the information consumer.

In fig. 5, the consumer behavior of two selection criteria 65 is shown in the vertical direction. Two audience targets 50 are used for desired and undesired behavior. The letter 'W' indicates a desired behavior and the letter 'DW' indicates an undesired behavior. The letter 'O' for openness indicates a situation in which no selection criterion 65 is applied to the information item 24.

In fig. 5, decision matrix 70a is used to indicate when information items 24 should be included or excluded from the information flow of consumer 28. In the decision matrix 70a, the letter T indicates that an information item 24 is included in the information stream, and the letter 'E' indicates that an information item 24 is excluded from the information stream. The symbol? The' indication system may decide whether to include the information item 24.

In fig. 6, the table expands the table shown in fig. 5 to show the priorities of audience targets 50 and selection criteria 65. The producer's audience target 50 is again in the vertical direction and the consumer selection criteria 65 are in the horizontal direction. Audience target 50 of the producer is shown to have a combined behavior and preference priority. The letter 'H' indicates high priority. The letter 'M' indicates medium priority. The letter 'L' indicates a low priority. In fig. 6, six audience target behavior and priority combinations are shown for producer 22. Six selection criteria behavior and priority combinations are shown for the consumer. As shown in fig. 5, the table also shows a case in which no behavioral targets 50 are applied to the information items 24 and a case in which no selection criteria 65 are applied to the information items.

Decision matrix 70b has the same meaning as fig. 5, but has been added with a combination symbol to illustrate the case where the system may overwrite the default. The symbol' I? ' indicates a situation where, in one embodiment, the items in the stream will be included by default but the system may decide to switch the decision. The symbol' E? ' indicates a situation where, in one embodiment, items in the stream will be excluded by default but the system may decide to switch the decision. Other symbols shown in fig. 6 have the same meanings as in fig. 5.

There is no limit to the number of discrete priority levels that may be assigned to audience target 50 or selection criteria 65. Fewer priority levels may also be allowed so that a combination between the table in fig. 5 and the table in fig. 6 is possible. For decision matrices 70 with discrete priority levels, the system can choose what cells to overwrite.

In one embodiment, the priority may be determined from a continuous function of the user profile 60 of the producer, the user profile 60 of the consumer, the metatag of the information item 24, external factors, or variables of any other data that may be used in the system. The priority of the continuous function may have any scale, and the scale may be infinite or fixed or normalized (e.g., normalized to zero or one interval).

For the case of sequential priorities, decision matrix 70c may include a logical function for each combination of producer 22 and consumer 28 behaviors in decision matrix 70c, as shown in FIG. 7. The logic function may evaluate the priority of producer and consumer behavior, along with other factors discussed below, to determine whether an information item 24 is included or excluded from the stream.

Any combination of the decision matrices 70 shown in fig. 5-7 may be possible. For example, a consumer may have several priority levels for wanted behavior and one priority for unwanted behavior, and a producer may have consecutive priorities for send behavior and three priorities for not send behavior.

In one embodiment, the processing of decision matrix 70 may be integral with information exchange 29 default distributor 26. In another embodiment, the processing of decision matrix 70 may be external to default distributor 26. In one embodiment, the processing of the decision matrix 70 may be distributed between the default distributor and external processing. In another embodiment, the decision matrix 70 may be partially evaluated to identify desirable consumers and the remaining processing of the decision matrix 70 may be completed to refine the consumers that will have the items contained in their streams.

In one embodiment, the producer sees a description of the priority of no-send similar to "never send", "preferably no send", "good bar" transitioning to high, medium, and low priority if they get it, but not included in my count ".

In one embodiment, the priority of behavior with respect to the consumer's intended behavior may be represented by a description similar to "must have", "have is good", and "give me if important" transitioning to high, medium, and low priorities.

In one embodiment, the decision grid 70d represents the decision matrix 70 in the case of discrete, continuous, or mixed priority as a two-dimensional interval, with each dimension having a range of [1, -1 ]. Unwanted and non-transmitted behaviors have their priority multiplied by-1 and the open case represented by 0. The two-dimensional interval is equivalent to any non-normalized two-dimensional interval. A threshold line 71 separates the interval from an inclusion region 72 and an exclusion region 73. A threshold line or boundary may be obtained from the metric 54 and may be represented by a threshold function, map, or relationship.

In one embodiment, exclusion zone 73 may be divided into reachable exclusion ranges and unreachable exclusion ranges. The achievable exclusion range may be defined as the portion of the exclusion area below the threshold line 71. The achievable exclusion range may also be defined as the portion of the exclusion range that is achievable by the producer if the producer can increase the priority of matching the consumer's audience goals.

In one embodiment, there may be priority bounds in the decision grid or decision matrix where the threshold lines may not cross.

In the discrete case, the threshold is a set of cells that form the boundary of the inclusion region 72 and the exclusion region 73. For example, in fig. 6, the threshold setting would be the limit along any row or column where there is a switch from inclusion to exclusion. The range or subset of the decision matrix 70 is a set of cells or regions in a two-dimensional interval.

Use of metrics

In one embodiment, the consumer engagement metric may be used as a metric of information item consumption or interaction with an information item. Consumer engagement metrics may be obtained or calculated from viewing, interacting, clicking, opening, or consumer consumption of information items and any other available indicator useful for information exchange. In one embodiment, the engagement metric may be accurate. In another embodiment, the engagement metric is evaluable. In one embodiment, the engagement metric may be the number of items engaged in a given phase.

In one embodiment, the specified stage consumer engagement metrics useful for information exchange may be stored in a database. In one embodiment, all historical data used to calculate or obtain the consumer engagement metric may be stored in a database.

In one embodiment, the engagement rate of the information consumer 28 may be measured as the number of engaged information items divided by the number of information items available for delivery or transmission to or to the consumer at a specified period (e.g., one day). In one embodiment, the engagement rate may be obtained from other sources including surveys, monitoring, or other internal and external metrics.

In one embodiment, a historical engagement rate may be calculated for each consumer. The historical engagement rate may be calculated from the prior engagement of the consumer in any of a number of ways. For example using a weighted history, rolling average or other calculation. Various measures of historical engagement may be used. In one embodiment, the historical engagement rate for each consumer may be maintained in a database. In one embodiment, all historical data used to calculate or obtain the consumer engagement rate metric may be stored in a database.

In one embodiment, the consumer item value of a consumer's information item may be evaluated using a priority established from the consumer's selection criteria. In one embodiment, the priority of the information item may be the highest priority that matches the selection criteria. In another embodiment, the consumer item value may be calculated from the priority of the overlapping selection criteria. In one embodiment, term values may be calculated from priorities and other metrics.

In one embodiment, a mapping of priority to a value of a consumer may be used. In another embodiment, it may be assumed that the consumer value and priority are comparable.

In one embodiment, an average consumer item value over a period of time may be calculated. The average consumer item value may be calculated as the sum of the consumer item values of the participating items over the period of time divided by the number of participating items over the period of time. In one embodiment, a weighted average may be used to calculate an average consumer term value, where the weights depend on information item metadata or other metrics. In one embodiment, a historical time series of average consumer item values may be calculated. In one embodiment, the historical time series of average consumer term values may be maintained in a database.

In one embodiment, a historical time series of average consumer item values may be used to estimate a consumer-expected item value for an information item that the consumer has not received. A number of formulas dedicated to the exchange of information can be used for this estimation. For example using a weighted history, rolling average or other calculation. A variety of measures of expected term values may be used. In one embodiment, the expected term values may be calculated from historical average consumer term values and other metrics.

In one embodiment, the expected term values may be calculated or obtained from surveys, sentiment analysis, or other metrics.

In one embodiment, a predicted engagement rate may be calculated. In one embodiment, the historical engagement rate and the internal and external metrics and signals may be used to derive a predicted engagement rate from statistical or predictive analysis. In one embodiment, the predicted engagement rate may be the same as the historical engagement rate.

In one embodiment, the engagement prediction map may correlate expected item values and predicted engagement levels. The predicted engagement level may represent the number of information items per specified phase. The participation prediction map may be a discrete, continuous or mixed logical function or map. In one embodiment, statistical methods applicable to information exchange may use consumer expectation term values and additional external and internal metrics and signals for calculating and deriving predictive participation formulas or mappings. In one embodiment, metrics from other consumers may be used to determine the engagement prediction mapping.

In one embodiment, an inverse engagement prediction map may be used to correlate engagement levels and expected term values.

In one embodiment, the producer item value per consumer may be a value at which the producer receives and consumes an information item for the consumer. Producer item values may be calculated using priorities established from audience targets for the information item. In one embodiment, producer term values for consumers may be calculated from priorities and other metrics.

In one embodiment, a mapping of priority to producer item value per consumer may be used. In another embodiment, the producer value and priority may be assumed to be comparable.

In one embodiment, the distribution of information items over a two-dimensional decision matrix 70 or decision grid may be calculated for each consumer. The distribution records the number of information items for each point in the decision matrix 70 or decision grid 70d over a period of time. Any number of techniques dedicated to information exchange may be used for historical data based distribution. For example using a weighted history, rolling average or other calculation. Multiple distributions are possible and may be used for different purposes of computing other metrics. In one embodiment, distributed aggregation across information consumers may be used.

In one embodiment, the historical distribution of information items and optionally additional metrics may be used to calculate a predicted distribution of information items for a current or future stage consumer. In one embodiment, the distribution of information items within a specified future phase may be predetermined.

In one embodiment, the target consumer expected item value may be calculated from the metrics to determine the expected item value for each consumer's information exchange.

In one embodiment, the threshold lines 71 of the decision matrix 70 or decision grid 70 may be calculated using a predicted distribution of information items for a consumer, a mapping of priorities to values of consumers, a mapping of priorities to values of producers, a participation prediction mapping, consumer expected item values, target expected item values, or other metrics.

In one embodiment, a swap value function may be specified to indicate a combined value to swap for each point on the grid. For example, the exchange value function may be T (p, c) ═ ap + bc, where p is producer and c is consumer, a is 1 if p >0 and 2 if p <0, b is l if c >0, and b is 2 if c < 0. This type of function encompasses tradeoffs when either the consumer term value or producer term value is negative. Other functions may be used depending on the purpose of the information exchange, and may vary with the customer, temporary parameters, or other internal or external parameters specific to the exchange. In one embodiment, the swap value function may define a priority bound.

In one embodiment, the number of information items over the distribution area of information items can be calculated as the sum of each point in the area. For example, the distribution may indicate that the number of items is 5, 4, 7, 3, 11 (for 5 points defining a particular region). The sum of the information items over this area is 30.

In one embodiment, the average consumer value over the information item distribution area may be calculated as the sum of consumers multiplied by the distribution value at each point in the area divided by the number of information items in the area.

In one embodiment, the threshold line 71 may be calculated for the distribution of information items as a region inside the decision grid 70d, where the predicted engagement level from the engagement prediction map specifying expected item values is approximately equal to the number of information items in that region. In one embodiment, the specified expected item value may be an average consumer value over the distribution area. In another embodiment, the specified expected item value may be determined from internal and external metrics.

In one embodiment, first, the inclusion region 72 may be selected for a specified distribution of information items by dividing the decision grid 70d into discrete points. For example, to divide the decision grid consumer and producer priority axis into l0, 20x 20 or 400 discrete points would be generated. For the decision matrix 70, the cells are used as discrete points. Second, the swap value function at each discrete point on the decision grid is evaluated to determine that the points are first included in the region. Third, the points are sorted in descending order of preference and the number of information items added at each point in the area is calculated, and the expected item value is also calculated using the average consumer value or other metric in the area to obtain the expected item value. Fourth, the predicted engagement level from the engagement prediction map specifying the expected item value is evaluated and stopped when the predicted engagement level is less than the number of items. Fifth, the processed points are used to define an inclusion region 72 and a threshold line 71.

In one embodiment, a distribution of information items that may be relevant to a consumer may be stored in a database. The distribution may be updated in real time. The threshold line 71 may be updated in real time as the distribution or other metric changes.

In one embodiment, consumer data collection may include selection criteria, distributions, decision matrices, threshold lines, and other consumer metrics. In one embodiment, consumer data collection may be stored on contiguous storage for quick access and processing.

In one embodiment, a consumer audience detail query method may be used to evaluate information items, audience targets, consumer data collections, or other internal metrics to determine a set of consumer audience details, which may include, but are not limited to, consumer priority, producer priority for the consumer, producer priority on a threshold line (if available), and an indicator of range (exclusion, inclusion, or reachable exclusion).

In one embodiment, the consumer audience detail query may use a column of metatag groups, fields, and values to evaluate consumer priority in response to metatags. The consumer audience detail query method logically evaluates the list of metatag groups, fields, and values against selection criteria to determine a consumer priority to be assigned to each metatag option in the list and may also include relevant combinations. The consumer priority in response to the metatag may include a priority level of the items in the list and may also include a summary by field, group, and selection combination.

In one embodiment, the audience detail query method may evaluate each consumer's consumer audience detail method to calculate and aggregate a set of audience details. Audience details may be presented to a metadata control loop, an audience restriction control loop, an audience targeting control loop, a system interface for entering audience targets, or a system interface for entering information items.

In one embodiment, statistical samples of sufficient size of consumer selection criteria and consumer data collection may be used in place of actual consumer data to provide an estimate of audience detail.

In one embodiment, audience details for various audience targets may include, but are not limited to, original audience size, increased audience size, cumulative audience size, priority imposed by audience size limits, audience size in an inclusion range, audience size in a reachable exclusion range, or average producer priority variation needed to move from reachable exclusion zones. Audience details for all specified audience targets may include, but are not limited to, the maximum audience size for all targets or the cumulative size of all targets. Audience details about an information item may include, but are not limited to, a distribution of consumer priorities about the information item, user profile summary statistics about a specified priority range, or priorities in response to meta tags.

In one embodiment, consumer priorities in response to metatags can be aggregated and summarized across all consumers to obtain responses for metatag values.

Control ring

A set of control loops use the metrics 54 to control the flow of information items in an information exchange. Metrics are metrics and parameters that may be internal or external to the information exchange. Sampling internal metrics include, but are not limited to, metrics related to producer, consumer, system information flow, or general information exchange. Sampling external metrics includes, but is not limited to, important sporting events occurring on the day, bad weather, day of the week, indications of political or business events occurring, measures of behavior outside of news and information flow or information exchange, flow behavior of external information exchange, historical projections, statistics, or any other relevant data.

One embodiment may have multiple control loops. Another embodiment may have a single control loop. Another embodiment may not have a control loop.

Decision matrix control loop

In one embodiment, the decision matrix control loop adjusts the threshold lines 71 or bounds in the decision matrix 70 or decision grid 70d that include the region 72 and the exclusion region 73 to improve or maintain a set of success metrics.

In one embodiment, an information item is included in the consumer's information stream if a point on the decision matrix 70 or decision grid 70d represented by the selection criteria priority and the audience target priority is within an inclusion area 72 defined by the consumer's threshold line 71.

In one embodiment, the decision matrix control loop may use a set of success metrics derived from the consumer, producer, information item, audience target, external source, or from a general system. Metrics related to the consumer include, but are not limited to, time to process the information stream, estimates of missing information items, engagement metrics, engagement rates, average selection criteria priority with respect to the information stream at recent and historical stages, consumer expected item values, predicted engagement rates, predicted engagement levels, or other consumer metrics. Metrics associated with a producer include, but are not limited to, user profile data including producer history, performance, or behavioral data. Metrics from external sources include, but are not limited to, indications of important sporting events, political or business events occurring on the day, inclement weather, day of the week, metrics of news and information external to the information exchange, or any factor deemed relevant to the prediction of consumer attention and appreciation. In another embodiment, only certain metrics or only one metric may be used.

In one embodiment, the decision matrix control loop may be part of the distribution subsystem 52.

In one embodiment, for each information item 24 processed by the decision control loop, consumer priority may be obtained from consumer selection criteria 65 and producer priority may be obtained from audience goals 50 for that information item.

In one embodiment, the plurality of information items may be processed at once as a distribution of information items on the decision grid 70d or decision matrix 70, and the inclusion region 72 may be computed to determine which information items may be included in the consumer's information stream. In one embodiment, information items may be delayed or queued to be evaluated together as a distribution of information items.

In one embodiment, an estimate of the probability that an information item will be missed, meaning that the information item will be received by the consumer but not processed by the consumer, may be derived from a metric. A system limit for the probability that an information item will be missed can be derived from the metric. Within a specified range or subset of the decision matrix 70, an information item is excluded if the estimated value of the probability that the information item will be missed is greater than the system limit of this estimation. In one embodiment, within a specified range or subset of the decision matrix 70, the information item is included if the consumer item value is greater than the consumer expected item value.

Producer limit control loop

Producer limit control loop 46 determines an audience size limit that is placed on a producer's audience target 50 at a particular priority level.

Fig. 9 illustrates an embodiment in which the audience size limit may be represented as an audience size limit mapping 75. The audience size limit mapping 75 may be used to obtain a priority for a given audience size or to obtain an audience size of a given priority. The audience size limit mapping 75 may be a function between the priority and the audience size limit or a relationship between the priority and the audience size limit. The mapping may be continuous, discrete, or mixed. Fig. 9 illustrates the audience size limit mapping 75 as a continuous mapping.

In one embodiment, the audience size limit mapping 75 may first be determined by the metrics 54. Metrics 54 may include, but are not limited to, the current number of information items flowing through the system, the relative number of consumers receiving too few or too many items, or the predicted number of information items flowing through the system in the future. The audience size limit mapping 75 may be adjusted using metadata and content of the information items 24, and may be further adjusted using metrics from the producer's user profile including, but not limited to, expertise, background, reputation, number of previous deliveries by the producer, interaction rate or performance of past deliveries of information items by the producer.

In one embodiment, the audience size limit mapping 75 may be dynamically determined in real-time. In one embodiment, the base audience size limit mapping may be set by acting as an administrator, and the base level set may or may not be adjusted in the producer limit control loop 46.

In one embodiment, the maximum audience size 76 may be determined from a maximum limit in the audience size limit mappings 75 having a positive priority. In one embodiment, the total audience that a producer may reach may not exceed the maximum audience size 76.

Fig. 10 illustrates one embodiment of a system interface for inputting audience target 44. Options 90 allow producer 22 to create audience targets 91, edit audience targets 92, reorder audience targets 93, manage audience target archives 94, view audience target details 95, view audience size limits 96, or complete the process when completed 97.

In one embodiment, producer 22 may interact with producer limit control loop 46 to first create an audience target or retrieve an audience target from an archive. After entering the first audience target, audience target may be evaluated by producer 22 and audience target details 95 may be viewed and processed. Producer 22 may accept priority levels, reorder audience targets 93, or edit audience targets 92. If a priority level of audience targets is accepted, the producer has the option to enter additional audience targets. If the producer enters additional audience targets, the process for the first audience target may be repeated. If the combined audience from all audience targets exceeds the maximum audience size 76, the producer may re-rank the audience targets 93 or edit the audience targets 92. In one embodiment, each additional audience target may have a lower or higher priority (in terms of ranking and audience target behavior) than the first audience target entered.

In one embodiment, audience targets may be evaluated in an order specified by the producer to determine an increased size of the audience targets. The increased audience size may be the size of additional audiences that are reachable by each subsequent lower ranked audience target. FIG. 9 identifies the ranked audience items as A1-A5. For example, consider the audience of audience targets Al, A3, and a4 as audience targets in which a producer wants to include or transmit information items, and consider a2 and a5 as audience targets in which a producer wants to exclude or not transmit. The audience for the target Al-a5 was evaluated for increased size. The increasing sizes with audience targets (in this example, a1, A3, and a4) that include behaviors may be accumulated down in order to obtain an accumulated size of audience targets. The increased audience excludes any consumers that would match one of the higher ranked audience targets. In one embodiment, the cumulative size of audience targets having an included behavior also adds an increased size of the audience targets. The cumulative size may then be evaluated from the audience size limit mapping 75 to determine a priority for the audience target. In fig. 9, audience targets Al, A3, and a4 have assigned priorities of P1, P3, and P4, respectively. In one embodiment, the cumulative size may be used as a lookup size to obtain a priority from the audience size limit mapping 75. In one embodiment, the cumulative size may be adjusted by a portion or all of the increased size.

In one embodiment, the increasing sizes of the audience targets with exclusion behavior (in this example, a2 and a5) may be accumulated down in order to obtain an accumulated size for each audience target with exclusion behavior. In one embodiment, the cumulative size of the audience targets with exclusion behavior does not add to the increased size of the audience targets. The cumulative size may then be evaluated from the audience size limit mapping 75 to determine a priority for the audience target. In fig. 9, audience goals a2 and a5 have assigned priorities P2 and P5, respectively.

In one embodiment, a producer may assign any priority to audience targets with exclusion behavior.

In one embodiment, using audience size limit mapping, audience goals may be automatically adjusted to improve audience size by: first, identify an audience to be included with a low priority; secondly, generating an audience target of the identified audience; third, directly assigning priorities to the audience targets and placing the priorities in the proper order of audience targets; and fourth, excluding the increased audience from the cumulative size and excluding the increased audience from the increased count of subsequent targets.

In one embodiment, assuming priority >0, audience targets with included behaviors may be processed. In one embodiment, audience targets that may have cumulative sizes that exceed the maximum audience size 76 less than the audience target increase size may be limited by the system to sizes that will not exceed the maximum audience size 76. In another embodiment, the producer may have the option to refine the audience target that crosses the limit.

In one embodiment, the audience size of the audience target may be an estimate, or in another embodiment, it may be an exact numerical value.

In one embodiment, the value of the audience target may be limited.

In one embodiment, a producer inputs one or more audience targets. The system then automatically ranks the audience targets by audience size and assigns priorities using the method described above.

In one embodiment, the producer may specify the priority preference as f (X) within a user profile range, where X is the user profile from a span of user profile characteristics. For example, the specified age is between 30 and 40, with 30 being most preferred. Using f (x), the ranking of each potential target user profile is obtained. The mapping between priorities is in the [0,1] interval, and the audience size limit mapping 75 is used to assign priorities to each add target from highest rank to lowest rank, and stops when the span of maximum audience size 76 or X is reached.

Method for determining audience size limit mappings

In one embodiment, the audience size limit mapping 75 may be determined using an inverse cumulative distribution of the priority of the consumer cluster density over the normalized interval [ -1,1] of the information items. The inverse cumulative distribution is the number of consumers whose selection criteria will register a given priority or higher for an information item. The consumer cluster density of an information item is the number of consumers at each consumer priority level, and this number can be obtained by accumulating the consumer count at each priority level. Next, the inverse distribution is obtained from the accumulation of cluster density starting at the top of the [ -1,1] interval. In one embodiment, only the cluster density and the inverse cumulative distribution over the interval [0,1] may be needed. In one embodiment, consumers with similar selection criteria may be aggregated in a mapping reduction representation with representative selection criteria and consumer numbers. This is so that only one representative consumer needs to be evaluated against a similar set of consumers. In one embodiment, the inverse cumulative distribution may be used directly as an audience limit map. In another embodiment, the inverse cumulative distribution may be scaled or adjusted before being used as the audience limit map. An advantage of using an inverse cumulative distribution is that producer 22 is provided with a higher limit that will have a natural consumer priority with respect to information item 24. For example, a popular merchant who provides information items for complimentary or a popular news agency with exclusive breaking news may have a large number of information consumers 28 who place a high priority when receiving such information items. In such cases, the producer may not need to enter any audience targets at all, as the default priority level is already high enough. On the other hand, a product vendor with an information item 24 that is a value marketing message to only a small group of information consumers may have a very strict audience limit mapping and may need to enter very specific audience targets.

In one embodiment, the audience limit mappings may be determined from a producer's transmission history, past behavior, and other mechanical analysis of keywords or information items.

In one embodiment, with discrete or continuous audience size limits, parameters of the audience size limit mapping may be determined from the metrics. For example, in the continuous case, a linear relationship between size and priority may be used, and the metric will determine the slope and intercept of the line. More specifically, in this linear example, where the priority is in the [0,1] interval, the parameter would be the priority level of the maximum audience size 76 and at audience size zero. Other mathematical functions, mappings and parameterized relationships may also be used, where parameters of such functions, mappings and relationships are determined by metrics in a similar manner.

Producer request control loop

The producer request control loop may take into account the effect of requesting the producer to provide additional metadata as well as the effect of audience targets on the number of information items that the producer may send or contribute over time and the perceived effect of the consumer adding information items. The producer requests that the control loop can use metrics including, but not limited to: the cost of opportunity for the producer's time, the time the producer takes to complete the transmission, the time additional requested data is obtained, the availability of additional data, the value of additional meta-tags or refinements, the time the producer takes to enter a new audience target or the time it takes to change, rank, or prioritize an audience target. Consumer perception, participation, or response metrics may also be used.

In one embodiment, the producer request control loop may be used to control the entry of information item metadata via metadata request control loop 42. In one embodiment, a producer request control loop may be used to request input by the control loop 48 to control audience targets 50. In one embodiment, the producer request control loop may supplement the producer limit control loop 46. In another embodiment, the producer request control loop may be an alternative to the producer limit control loop 46.

In one embodiment, the subject domain of an information item may be determined by automatic analysis of the information item at the time of author input. The topic domain can be used to obtain a list of metatag schemes and determined usage data for the topic domain. The list of metatag schemes and usage data may be used to generate a list of metatag groups, fields, and values that may be provided to an audience detail query method. Responses to metatags with an audience size increment for specifying metatags that may be provided by an audience detail query method may be used to obtain an audience size increment that may be used to select the order in which metatag questions are requested.

In one embodiment, the list of metatag schemes may be generic. In another embodiment, the meta-tagging scheme may be specific to the subject domain.

In one embodiment, the list of metatag schemes may be selected to complete metadata that cannot be reliably provided by automated analysis or to confirm known fields or to confirm the expertise of the producer. This may also be used to limit autonomous and non-autonomous producers.

In one embodiment, the producer may not be able to see the audience size associated with a given metatag.

In one embodiment, the desired audience distribution may be used to regulate the producer request control loop. Audience distribution may be parameterized by statistical measures or metrics to quantify desirability. For example, to have a distribution over the consumer priority interval that reduces the density to zero or near zero.

In one embodiment, the metadata request control loop may use audience distribution in the consumer priority interval. Using audience distribution in the consumer priority interval has the advantage that: well-known producers with naturally high audiences and receptivity can avoid the additional need for metatags or audience targets and the associated time burden.

In another embodiment, a metadata request control loop or audience request control loop may use audience distribution on a two-dimensional decision grid or decision matrix, and the desired audience distribution may be on both consumer and producer priorities. The information producer may request the control loop using metadata or the audience to request the control loop to achieve a desired audience distribution.

In one embodiment, any required metadata requests or audience target requests may be stopped when the desired audience distribution has been reached or a maximum number of requests to the producer have been made.

The subject field may be compared to a subject field that may be inferred from the user profile of the producer and previous items of information generated by the producer. If the implied subject field of the information item does not align with the subject field of the producer and the history of the producer, the metadata questions may be asked to confirm the validity of the post and the sender.

Control loop management

An administrator of the information exchange may select a control loop to use and set the configuration of the control loop. The success metric may be oriented to balance the value to different exchange users with the goals of information exchange stakeholders.

Conclusion

The computer system described herein is widely applicable to existing information exchanges or as a basis for new information exchanges to optimize and better attract participants.

The examples and variations given herein are not to be taken in a limiting sense and other examples and variations will be apparent to those skilled in the art.

Claims

1. An information exchange apparatus implemented on a computer system, the information exchange apparatus comprising:

a user profile store for storing user profile data;

a distributor for communicating information items from an information producer to information consumers, wherein the distributor delivers an information stream to each information consumer, and wherein the information stream is a set of information items;

a system interface for entering an information item;

a distribution subsystem for processing the information items, audience goals, metrics used to control the flow of information items in the information exchange, user profiles, and selection criteria to determine which information consumers should get, receive, or see the information items; wherein the distribution subsystem is configured to:

determining a distribution of information items, wherein the distribution is over a two-dimensional range of priorities, and wherein the distribution is specific to the information consumer, and wherein the metric is used at least in part to determine the distribution of information items;

for the region of the distribution of information items, determining:

(a) the number of the items is such that,

(b) the value of the desired term is,

(c) a success metric, wherein the success metric is determined using the expected item value and the quantity of items, and wherein a statistical relationship between expected item values and predicted engagement levels of the information consumer is used;

selecting an inclusion region, wherein the inclusion region is preferred over other regions based at least in part on the success metric;

obtaining an information item from the system interface for inputting an information item, wherein the information item originates from the information producer;

obtaining a user profile of the information producer and a user profile of the information consumer from the user profile store;

obtaining selection criteria related to or matching the information item;

determining a consumer priority for the information item using the selection criteria and the information item or a user profile of the information producer;

obtaining audience targets that apply to the information items and match the information consumer's user profile;

determining producer priorities for the information items, wherein the priorities are determined using a user profile of the information consumer and the audience targets;

determining that the information item is included into the information stream of the information consumer, wherein the consumer priority, producer priority, and include region are used in the determining; and is

Whereby the information item is delivered or delivered to the information consumer when the information item is in the information stream.

2. A social network using the information exchange device of claim 1, wherein the information item is a post from a first user of the social network, and wherein the information item is delivered or communicated to a second user of the social network via an information stream.

3. An advertising network using the information exchange apparatus of claim 1, wherein the information item includes an advertisement, a bid, or a bid price.

4. A distribution system using the information exchange apparatus of claim 1, wherein the one or more information items include articles, stories, messages, notifications, video, or audio received from at least one information producer.

5. An information exchange method implemented on a computer system for operating as an information exchange with a distributor for communicating information items from an information producer to information consumers, wherein the distributor delivers an information stream to each information consumer, and wherein the information stream is a collection and or set of information items delivered to the information consumers sequentially or together via a medium delivering information; a decision matrix for controlling, at least in part, which information items are communicated or delivered to the information consumer; the system interface is used for inputting information items; a user profile store; and a metric, wherein the metric is used to control the flow of information items in the information exchange; and comprises the steps of:

for the information consumer, determining:

a distribution of information items, wherein the distribution is over a dimension of the decision matrix, and wherein the metric is used at least in part to determine the distribution of information items,

an engagement prediction map, wherein the map relates expected item values and engagement levels, and wherein the metric is used at least in part to determine the engagement prediction map;

for the region of the distribution of information items, determining:

(a) the number of the items is such that,

(b) the value of the desired term is,

(c) a predicted engagement level determined from the engagement prediction map of the expected item values;

comparing regions of the distribution and selecting an inclusion region, wherein the inclusion region is closer in proximity to the predicted engagement level and the number of items than other regions of the distribution of information items;

obtaining selection criteria related to or matching the information item;

determining whether the information item is in the inclusion region using the producer priority and the consumer priority; and

including the information item in the information stream of the information consumer when the information item is in the inclusion region;

6. The method of claim 5, further comprising:

a swap value function; and

wherein any term or point in the inclusive region is equal to or greater than any term or point outside the inclusive region according to the swap value function.

7. The method of claim 5, wherein the audience target maps allocated producer priorities to values of one or more user profile parameters.

8. The method of claim 5, wherein the selection criteria maps an assigned consumer priority to a value of one or more user profile variables or information item variables.

9. The method of claim 5, further comprising:

collection of information items;

obtaining an order of the items;

evaluating each item in said order obtained for inclusion into the information stream.

10. The method of claim 5, further comprising:

a set of information items;

obtaining a distribution of the information items based on the set of information items;

evaluating each item in the set of information items for inclusion in an information stream according to the inclusion region.

11. The method of any of claims 5-8, wherein the decision matrix resolves a conflict between the consumer priority and the producer priority.

12. A social network, comprising:

a post from a first user of the social network;

news delivery of a second user of the social network, wherein the second user has associated, followed, subscribed to, or agreed to receive a post from the first user;

a distribution subsystem for determining that the post is included in the news delivery using the method of any of claims 5-9.

13. An advertising network, comprising:

one or more informational items, whereby the informational items include an inquiry, bid, or advertisement;

use of a method according to any of claims 5-11 for evaluating a distribution subsystem of the inclusion of the one or more information items in an information stream of at least one information consumer.

14. A publication system, comprising:

one or more information items, whereby the information items comprise articles, stories, price enquiries, bids, advertisements, messages, notifications, video, or audio received from at least one information producer;

15. An information exchange apparatus comprising:

an information item obtained from a first user of the information exchange device;

a second user of the information exchange device allowed to receive the information item via the distributor;

use of the method of claim 5 to determine a distribution subsystem for which the information item is excluded from the information flow of the second user.