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 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 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 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 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 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 method and system for temporally sequenced recommendations and associated explanations in subscription-based systems delivers to users of a subscription-based system multiple recommended objects that are arranged in a temporal sequence. The delivered recommended objects are in accordance with user subscriptions and inferences of preferences that are based, at least in part, on usage behaviors. Variations of the system and method include delivering recommended objects in accordance with the contents of the objects and user direct feedback with regard to the objects. Information as to why objects were delivered to users is provided to the users.

Abstract: A method and system for temporally-sequenced recommendations in subscription-based systems delivers to users of a subscription-based system multiple recommended objects that are arranged in a temporal sequence. The delivered recommended objects are in accordance with user subscriptions and/or inferences of preferences that are based, at least in part, on usage behaviors. Variations of the system and method include delivering recommended objects in accordance with the contents of the objects and user direct feedback with regard to the objects. Information as to why objects were delivered to users may be provided to the users.

Abstract: A method and device for temporally sequenced recommendations of activities delivers to users temporally sequenced objects comprising user activities, wherein the delivered objects are selected based, at least in part, on inferences of preferences from usage behaviors. The delivered objects may include activities associated with processor-based devices in addition to human activities. Variations of the system and method include delivering the temporally sequenced objects in accordance with the contents of the objects and user feedback with regard to the objects. Information as to why objects were delivered to users may be provided to the users.

Abstract: An explanatory people matching system and method matches people in accordance with inferences from usage behaviors, and delivers reasons for the matching to the matched people. The usage behaviors may include behaviors such as subscriptions and accesses to computer-implemented objects. The reasons for the matching that are delivered to the matched people may include references to usage behaviors, inferences of common interests, and/or expressions of interest between the matched people. Additional explanatory information may be provided upon request by the matched people.

Abstract: A computer-implemented system and method for expertise-responsive adaptive workflow delivers workflow step recommendations based, at least in part, on inferences from behavioral information and a level of expertise. Experts may be designated manually or automatically, and may inform the generation of the recommendations. The degree to which behavioral information of designated experts influences recommendations may be tuned by recommendation recipients. Variations of the system and method include generating the recommendations in accordance with physiological responses and/or monitored physical locations. Information as to why recommendations were delivered to recommendation recipients may be provided to the recommendation recipients.

Abstract: A computer-implemented system and method for adaptive pricing determines a price that is associated with one or more recommended products and/or product configurations based, at least in part, on inferences from behavioral information. The price may be further determined in accordance with factors such as location, product costs, inventory levels, a physical site, and/or a bidding process.

Abstract: A system and method for computer-implemented adaptive games selects and arranges game objects in game instances in accordance with inferred preferences based, at least in part, on usage behaviors. Variations of the system and method include automatically selecting and/or arranging the game objects in accordance with the contents of the game objects, physical location, and physiological responses. Soundtracks may be selected in accordance with selected game objects. Information as to why game objects were selected for inclusion in game instances may be provided to game instance recipients.

Abstract: A computer-implemented context-conditional recommendation delivery method, system, and device conditionally enable delivery of recommendations that are based on inferred preferences, given a sufficient contextual basis. The contextual basis may include factors such as location, customer contextual information and/or supplier contextual information. The recommendations may be delivered to another processor-based device, and may comprise one or more products and a corresponding price. Information as to why the recommendation was delivered to a recommendation recipient may be provided to the recommendation recipient.

Abstract: A computer-implemented context-conditional recommendation receipt method, system, and device conditionally enable receipt of recommendations that are based on inferred preferences, given a sufficient contextual basis. The contextual basis may include factors such as location, supplier contextual information, and/or product attributes. The recommendations may be generated by another processor-based device, and may comprise one or more products and a corresponding price. Information as to why the recommendation was delivered to a recommendation recipient may be provided to the recommendation recipient.

Abstract: A system and method for temporally sequenced recommendations in subscription-based systems delivers to users of a subscription-based system multiple recommended objects that are arranged in a temporal sequence. The delivered recommended objects are in accordance with user subscriptions and/or inferences of preferences that are based, at least in part, on usage behaviors. Variations of the system and method include delivering recommended objects in accordance with the contents of the objects and user direct feedback with regard to the objects. Information as to why objects were delivered to users may be provided to the users.

Abstract: A system and method for computer-implemented adaptive soundtracks selects and arranges songs in media instances in accordance with inferred preferences based, at least in part, on usage behaviors. Variations of the system and method include automatically selecting and/or arranging the songs in accordance with the contents of the songs, the tagging of the songs, a physical location, and physiological responses. Information as to why songs were selected for inclusion in media instances may be provided to media instance recipients.

Abstract: A system and method for temporally sequenced recommendations of activities delivers to users temporally sequenced objects comprising user activities, wherein the delivered objects are selected based, at least in part, on inferences of preferences from usage behaviors. The delivered objects may include activities associated with processor-based devices in addition to human activities. Variations of the system and method include delivering the temporally sequenced objects in accordance with the contents of the objects and user feedback with regard to the objects. Information as to why objects were delivered to users may be provided to the users.

Abstract: A site-contextualized method, system, and device generates recommendations based on usage behaviors and a physical site layout. The recommendations may comprise one or more products, and a recommendation may be accompanied with an associated price. The recommendations may be based on an inference of a preference and/or in accordance with a physical object. Information as to why the recommendation was delivered to a recommendation recipient may be provided to the recommendation recipient.

Abstract: A physiological-responsive computer-implemented system and device generates recommendations based on usage behaviors, including physiological responses such as body movements and gestures. The recommendations may be based on an inference of a preference and/or in accordance with the location or proximity of a physical object, and may be delivered in the context of the recommendation recipient interacting with a computer-implemented game. Information as to why the recommendation was delivered to a recommendation recipient may be provided to the recommendation recipient.