Abstract: An integrated search and adaptive discovery system and method integrates contents-based indexing and behavioral-based indexing of collections of computer-implemented objects to generate contextualized and/or personalized recommendations. The degree to which contextualization and/or personalization criteria are applied in generating recommendations can be tuned by the recommendation recipient. Personalization functions are applied that are informed by inferred interests and/or expertise, and personalization vectors can be transformed into a format executable by a search engine. Explanations may be provided to recommendation recipients as to why they received recommendations.

Abstract: A multi-social network discovery system and method enables users to select multiple social networks comprising activity streams and to receive recommendations sourced from the selected multiple social networks. The multi-social network discovery system and method generates affinity values based on inferred affinity levels between topics, which may be associated with tags, and objects that are associated with the activity streams. The inferred affinity values may be derived from usage behaviors such as tagging and/or based on an automatic analysis of the content of the objects. The affinity values inform the selection of the objects to be recommended to a user, and the selection may be further informed by inferred affinity levels between the user and the topics. The recommendations may be tuned by the user through application of a recommendation tuning control.

Abstract: A comparative expertise level-based discovery system infers an expertise level associated with a system user from a plurality of usage behaviors and compares this expertise level with expertise levels associated with computer-implemented objects. One or more of the computer-implemented objects are selected for delivery to the user in accordance with a comparison of the inferred expertise level of the user and the expertise levels associated with the computer-implemented objects. The expertise levels associated with the computer-implemented objects may be inferred from information within the objects such as text and/or may be inferred from usage behaviors. Inferences of interests of the user may further inform the selection of one or more of the computer-implemented objects for delivery to the user. Explanations for the delivery of the computer-implemented objects may be provided to the user.

Abstract: A system and method of advertising based on the automatic determination of an advertising recipient's location and inferences of preferences derived from usage behaviors is disclosed. The advertising recipient's location that is considered in delivering a specific advertisement may be the current location or one or more historical locations. Proximity considerations with regard to other people, locations, or physical objects may influence the advertisement that is to be delivered. The advertisement may be delivered to a mobile device that has location-aware capabilities such as a Global Positioning System-based system.

Abstract: A multi-social network discovery system and method enables users to select multiple social networks comprising activity streams and to receive recommendations sourced from the selected multiple social networks. The multi-social network discovery system and method generates affinity values based on inferred affinity levels between topics, which may be associated with tags, and objects that are associated with the activity streams. The inferred affinity values may be derived from usage behaviors such as tagging and/or based on an automatic analysis of the content of the objects. The affinity values inform the selection of the objects to be recommended to a user, and the selection may be further informed by inferred affinity levels between the user and the topics. The recommendations may be tuned by the user through application of a recommendation tuning control.

Abstract: A computer-implemented transformational protocol is applied to contextually transform an originating computer-implemented structure into a fuzzy network-based structure. The originating structure may be a hierarchy, for example. The fuzzy network-based structure is adaptively modified based on behavioral information, and recommendations delivered to users may be generated in accordance with the fuzzy network-based structure and inferences from a plurality of usage behaviors. The recommendations may be generated in accordance with a network neighborhood within the fuzzy network-based structure that is based, at least in part, on a navigational context within the originating structure.

Abstract: A comparative expertise level-based discovery system infers an expertise level associated with a system user from a plurality of usage behaviors and compares this expertise level with expertise levels associated with computer-implemented objects. One or more of the computer-implemented objects are selected for delivery to the user in accordance with a comparison of the inferred expertise level of the user and the expertise levels associated with the computer-implemented objects. The expertise levels associated with the computer-implemented objects may be inferred from information within the objects such as text and/or may be inferred from usage behaviors. Inferences of interests of the user may further inform the selection of one or more of the computer-implemented objects for delivery to the user. Explanations for the delivery of the computer-implemented objects may be provided to the user.

Abstract: A computer-implemented transforming function contextually transforms multiple originating computer-implemented structures into a composite fuzzy network-based structure. The composite fuzzy network-based structure serves as a contextual basis for recommendations. The recommendations may be generated in accordance with inferences of preferences and interests based on usage behaviors. The recommendations may further be generated in accordance with a network neighborhood within the composite fuzzy network-based structure that is based, at least in part, on a navigational context within an originating structure and a user-established navigational neighborhood scope.

Abstract: A computer-implemented adaptive expertise clustering system and method generates expertise cohorts based on inferences from behavioral information and applies the expertise cohorts in generating recommendations for delivery to users. The expertise clustering may be informed by the evaluation of the contents of computer-implemented objects. Behaviors associated with members of specific expertise cohorts after specific events may inform the recommendations. Recommendations may comprise, for example, other users or process steps. Explanations for the recommendations may be provided to recommendation recipients.

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: A computer-implemented discovery system applies a contextual neighborhood scope with respect to a fuzzy network-based structure, and generates recommendations in accordance with the resulting contextual neighborhood and inferences from behavioral information. The contextual neighborhood scope may be established by a user, and the basis for the contextual neighborhood may be in accordance with an object that is accessed by a user. The inferences may be embodied in affinity vectors, and may include inferences of user preferences and levels of expertise.

Abstract: A computer-implemented activity stream-based recommendations system delivers recommendations in accordance with a selected item of an activity stream, inferences of interests based on usage behaviors, and a contextual neighborhood of objects. In addition, or alternatively, the recommendations may be generated in accordance with an inference of expertise. The contents of the objects in the activity stream may be generated by humans or automatically by a processor-based device. Explanations for the recommendations may be delivered to recommendation recipients.

Abstract: A computer-implemented portable inferred interests and expertise profiling system generates an inferred user profile embodied as an inference vector, which is generated based on inferences from behavioral information and in accordance with standardized tags that are associated with topical areas in a first system. The inferences may be of, for example, interests or levels of expertise. Recommendations are then generated based on the inference vector and a different set of topics. Multiple inference vectors may be combined into a composite inference vector, which can serve as a basis for recommendations generated with respect to different sets of topics than those that informed the generation of the composite inference vector.

Abstract: A computer-implemented serendipitous recommendations system and method generates recommendations for delivery to system users in accordance with settings of desired levels of serendipity, including serendipity levels established through use of serendipity tuning controls operable by users. The recommendations are informed by an interest affinity anomaly function that identifies contrasting interest affinities between recommendation recipients and other users. Explanations may be generated that provide reasons as to why a recommendation was delivered to a user, and the explanation may include a selection of phrases that are influenced by a serendipity level setting, and may include an expression of a level of confidence with regard to the recommendation.

Abstract: A computer-implemented expertise discovery method and system generates recommendations of people in accordance with inferences of levels of expertise from behavioral information. The generation of the recommendations may be in accordance with comparisons of two or more expertise vectors and associated correlations between the vectors. The inferences of expertise may be with respect to specified topical neighborhoods. Explanations as to why recommendations were made are delivered to the recommendation recipients, and may include references to the inferred levels of expertise, the behavioral bases for the inferences, and/or the confidence in the inferences.

Abstract: A relationship-based people matching system and method matches people in accordance with inferences of preferences from usage behaviors that include the explicit establishing of relationships, including directionally distinct relationships, by users. The usage behaviors may further comprise behaviors such as establishing subscriptions and the tagging of computer-implemented objects. The matching may be performed in response to a search request. Expressions of interest between matched people may be performed by the matched people through establishment of computer-implemented relationships. An explanation for the matching of matched people that references computer-implemented relationships may be delivered.

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: An adaptive recommendation system and a mobile adaptive recommendation system are disclosed. The adaptive recommendation system and the mobile adaptive recommendation system include algorithms for monitoring user usage behaviors across a plurality of usage behavior categories associated with a computer-based system, and generating recommendations based on inferences on user preferences and interests. Privacy control functions and compensatory functions related to insincere usage behaviors can be applied. Adaptive recommendation delivery can take the form of visual-based or audio-based formats.

Abstract: An adaptive system applied to social network and process network applications is disclosed. These adaptive social networking and tagging systems, and adaptive process network systems, apply algorithms for monitoring user usage behaviors across a plurality of usage behavior categories associated with a computer-based system, and generating recommendations based on inferences on user preferences and interests based, at least in part, on usage behaviors. Additional functions for effectively and efficiently managing social and process networks, such as social network measurement and analysis features, community tagging functions, and process network recombination functions are also described.

Abstract: An adaptive self-modifying system, a recombinant fuzzy network system, and an adaptive recombinant system are disclosed. The adaptive self-modifying system includes functions and algorithms for the self modification of structural elements, including relationships among system objects of the adaptive self-modifying system, based at least in part on usage behaviors associated with the system. The recombinant and adaptive recombinant systems feature functions for syndicating and combining fuzzy networks. The combining of the structural elements of the adaptive recombinant system may be based, at least in part, on usage behaviors associated with the system, and may be preferentially recombined based on evaluations of network “fitness.