Abstract: A method and system of expertise discovery in social networks accesses behavioral information associated with users' use of a computer-implemented social network, whereby the computer-implemented social network enables its users to have subscribing relationships with other users. Expertise levels of users of the social network with respect to one or more topics are automatically inferred from the behavioral information. The behavioral information may comprise interactions by other users with respect to content published by a user. The expertise levels of users may be inferred in accordance with the expertise levels of other users who are subscribed to them. Recommendations of users based on their expertise levels may be delivered, as well as explanations for the recommendations. Expertise levels of multiple users and/or expertise levels of a user over time may be delivered for display.

Abstract: An adaptive probabilistic computer-controlled method and system determines computer-controlled actions that are expected to reduce uncertainties that are embodied as probabilities and then updates the probabilities in accordance with the results of the computer-controlled actions. The updated probabilities inform the determination of subsequent computer-controlled actions. Expected values of information may also be applied in determining the subsequent computer-controlled actions. The computer-controlled actions may comprise analysis of content and generating recommendations that are delivered to users, and inferences from the analysis of content and usage behaviors may inform the updating of the probabilities. Statistical learning functions and/or neural networks may be applied in determining the computer-controlled actions.

Abstract: A stream of attention method, system, and apparatus determines a first focus of attention by applying a probabilistic analysis to a first set of syntactical elements. The syntactical elements may be derived from external communications or from communications generated internally by a computer-implemented system. Alternatively, the syntactical elements may be determined based on their association with images accessed from an external or internal source. Actions are performed by the computer-implemented system that are expected to reduce uncertainty with respect to the focus of attention. Such actions may include posing syntactical-based interrogatives directed to external entities or internally, searching a corpus of content, and/or moving to a new location and receiving input at the new location.

Abstract: A method and system of expertise discovery in social networks accesses behavioral information associated with users' use of a computer-implemented social network, whereby the computer-implemented social network enables its users to have subscribing relationships with other users. Expertise levels of users of the social network with respect to one or more topics are automatically inferred from the behavioral information. The behavioral information may comprise interactions by other users with respect to content published by a user. The expertise levels of users may be inferred in accordance with the expertise levels of other users who are subscribed to them. Recommendations of users based on their expertise levels may be delivered, as well as explanations for the recommendations. Expertise levels of multiple users and/or expertise levels of a user over time may be delivered for display.

Abstract: A serendipity generating method, system, and device generates recommendations by identifying contrasting corresponding topic affinity values between users who more generally have a relatively high degree of similarity between corresponding topic affinity values. The topic affinity values may represent user interests with respect to topics and the topic affinity values may be inferred from usage behaviors, including geographic location information. Recommendations may be further in accordance with an assessment of the amount or quality of the usage behaviors from which the topic affinity values are derived and/or in accordance with the application of a probabilistic process. The recommendations may comprise computer-implemented objects that have relatively high affinities to topics associated with the contrasting corresponding topic affinity values.

Abstract: An adaptive probabilistic computer-controlled method and system determines computer-controlled actions that are expected to reduce uncertainties that are embodied as probabilities and then updates the probabilities in accordance with the results of the computer-controlled actions. The updated probabilities inform the determination of subsequent computer-controlled actions. Expected values of information may also be applied in determining the subsequent computer-controlled actions. The computer-controlled actions may comprise analysis of content and generating recommendations that are delivered to users, and inferences from the analysis of content and usage behaviors may inform the updating of the probabilities. Statistical learning functions and/or neural networks may be applied in determining the computer-controlled actions.

Abstract: A self-referential semantic-based method, system, and device links semantic chains comprising subject, predicate, and objects of the predicate and optionally, an associated probability, with behavioral-based chains comprising a computer-implemented system, predicate, and an object of the predicate, and generates self-referential communications based on the linked chains. The self-referential communications may include metaphorical and/or imaginative constructs, and may convey a sense of confidence or what has been learned over time in accordance with weightings associated with the semantic and/or behavioral chains. The self-referential semantic-based device may take the form of a self-propelled apparatus, including an apparatus in humanoid form.

Abstract: A directed expertise level-based discovery system, method, and device infers an expertise level associated with a system user from a plurality of usage behaviors and delivers one or more computer-implemented objects to the user that are selected in accordance with the inferred expertise level and a directionally distinct relationship between computer-implemented objects. The directed expertise level-based discovery system, method, and device may infer the expertise level from textual information and/or in accordance with calibration information such as test results. Geographic location awareness associated with a portable device may inform the selection of the computer-implemented objects to be delivered to the user. An explanation may be delivered to the user that references information that is used to inform the selection of the computer-implemented objects to be delivered to the user.

Abstract: A content-based expertise level inferencing system and method analyzes content such as text-based content that is associated with a user and infers an expertise level of the user that is associated with a topic. Language-based analytic techniques may be applied in inferring the expertise level, including the application of domain-specific vocabulary and frequency analyzes. The expertise level may be further inferred based on usage behaviors of the user and/or other users. Calibration information may be applied in generating the inference of the expertise level. Recommendations may be generated based on the inferred expertise level, and explanations for the recommendation and/or the inference may be generated for delivery to a recommendation recipient.

Abstract: A personalized activity stream system, method, and device delivers a recommendation that is contextualized basis a selected item in an activity stream and that is personalized for a recommendation recipient consistent with an inference of the recommendation recipient's interests. Contextualization and personalization may be based on an automatic analysis of usage behaviors and/or content. The recommendation may be informed by an inference of an expertise level, and an explanation for the recommendation may be delivered to the recommendation recipient. The recommendation may be in accordance with an automatically determined geographic location associated with a location-aware portable device, as well as environmental conditions proximal to the portable device.

Abstract: A computer-implemented adaptive experimentation method and system is described that automatically selects and executes information gathering actions. The adaptive experimentation method and system integrates value of information considerations, experimental design, and inferences from experimental results. The experimental results may include behaviors of users of a computer-based system. The process enables an automatic, adaptive process for attaining additional information and applying the attained information in making subsequent experiment decisions.

Abstract: A physical object-based people matching method, system, and device recommends a first person to a second person based on the geographic proximity of a location associated with the first person to one or more physical objects that are associated with the second person. The geographic proximity may be determined from the use of a location-aware portable device. The recommendation may be further informed by a mobility inference. The first person is enabled to send an expression of interest to the second person in response to the recommendation, and the second person may be enabled to send an expression of interest to the first person in response to receiving the expression of interest. An explanation for the recommendation may be delivered to the first person that references a mobility inference or a computer-implemented map.

Abstract: An inferential people matching method, system, and device infers people's mutual interests in making contact with each other from usage behaviors associated with computer-implemented systems. The usage behaviors may comprise monitored geographic location information that is associated with portable processor-based devices. In accordance with the inference of mutual interest, recommendations of each of two people are generated for delivery to the other of the two people. Expressions of interest between the two people are determined based on usage behaviors exhibited by the two people in response to the recommendations. These post-recommendation usage behaviors may include gestures and physiological responses. A bilateral expression of interest is revealed to the two people if the bilateral expression of interest that is determined from the expressions of interest is sufficient.

Abstract: A location-based matching system and method matches users of a computer-implemented system in accordance with inferences of mutual interest between the users, whereby the inferences of mutual interest are based on mutual computer-implemented relationships and user location information. Inferences of mutual interest may also be in accordance with a physical object. The inferences of mutual interest may be with respect to interests by users in being physically proximal to each other or being physically proximal to a location associated with one of the users. Information associated with user matches may be delivered to a matched user in the form of a computer-implemented map.

Abstract: A mutual expressions of interest people matching system and method recommends people to other people in accordance with inferences of mutual interests from usage behaviors. A person who receives a recommendation can express an interest to a recommended person, and the expression of interest can be an explicit indication of interest in establishing a computer-implemented relationship or the establishing of a directional distinct relationship between the recommended person and the recommendation receiver. The recommended person can in response express an interest to the recommendation receiver by establishing a computer-implemented relationship between the parties. The relationship between the parties may be a subscription-based relationship and explanations for the recommendations may be provided to recommendation receivers.

Abstract: An adaptive probabilistic semantic computer-implemented system and method links semantic chains comprising subject, predicate, and object triples and an associated probability with behavioral-based chains comprising a system user, predicate, and object triples, and infers user preferences based on the linked chains. Recommendations are generated for delivery to system users based on the preference inferences. Explanations may be provided to recommendation recipients as to why they received recommendations, and the explanations may convey a sense of confidence in accordance with probabilistic semantic associations.

Abstract: A directed expertise level-based discovery system, method, and device infers an expertise level associated with a system user from a plurality of usage behaviors and delivers one or more computer-implemented objects to the user that are selected in accordance with the inferred expertise level and a directionally distinct relationship between computer-implemented objects. The directed expertise level-based discovery system, method, and device may infer the expertise level from textual information and/or in accordance with calibration information such as test results. Geographic location awareness associated with a portable device may inform the selection of the computer-implemented objects to be delivered to the user. An explanation may be delivered to the user that references information that is used to inform the selection of the computer-implemented objects to be delivered to the user.

Abstract: A content-based expertise level inferencing system and method analyzes content such as text-based content that is associated with a user and infers an expertise level of the user that is associated with a topic. Language-based analytic techniques may be applied in inferring the expertise level, including the application of domain-specific vocabulary and frequency analyses. The expertise level may be further inferred based on usage behaviors of the user and/or other users. Calibration information may be applied in generating the inference of the expertise level. Recommendations may be generated based on the inferred expertise level, and explanations for the recommendation and/or the inference may be generated for delivery to a recommendation recipient.

Abstract: A personalized activity stream system, method, and device delivers a recommendation that is contextualized basis a selected item in an activity stream and that is personalized for a recommendation recipient consistent with an inference of the recommendation recipient's interests. Contextualization and personalization may be based on an automatic analysis of usage behaviors and/or content. The recommendation may be informed by an inference of an expertise level, and an explanation for the recommendation may be delivered to the recommendation recipient. The recommendation may be in accordance with an automatically determined geographic location associated with a location-aware portable device, as well as environmental conditions proximal to the portable device.

Abstract: A method and system for generating adaptive explanations for associated recommendations is disclosed. The adaptive explanations comprise a syntactical structure and associated phrases that are selected in accordance with usage behaviors and/or inferences associated with usage behaviors. The phrases included in an adaptive explanation may be selected through application of a non-deterministic process. The adaptive explanations may be beneficially applied to recommendations that are associated with content, products, and people, including recommendations that comprise advertisements.