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 integrated interest and expertise-based discovery system and method infers interest and expertise levels of users and generates recommendations for delivery to the users in accordance with the inferred interest and expertise levels. The inferences of expertise may be relative to other users and may be combined with expertise calibration information in generating expected expertise levels for users with respect to one or more topics. The generation of recommendations may be informed by the degree of inferred interest in topical areas, by the comparison of expertise levels among users, and by information regarding the user expertise levels that are appropriate with respect to specific items of content. Explanations may be provided to recommendation recipients as to why they received recommendations.

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: 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: An environment-responsive people matching system and method matches people in accordance with inferences of mutual interests based, at least in part, on environmental conditions local to the people being matched. Other behavioral information, proximity, and mobility inferences may also influence the matching. Relationships with varying degree of affinity between matched people may be generated in accordance with the inferences. Explanations for the suggested matches may be delivered to the matched people.

Abstract: A computer-implemented system and method for adaptive product bundling and pricing determines a bundle of products, or a configuration of one or more products, and a price for the bundle or configuration, based on behavioral information associated with one or more users of one or more computer-implemented systems. The product bundle or configuration prices may be further determined in accordance with inferences of price sensitivities, pricing experimentations, expectations of future transactions, inventory levels, costs, and collective price formation processes.

Abstract: A people matching system and method matches people in accordance with inferences of mutual interests, location, and/or proximity. Suggested matches may also be based on factors such as mobility inferences and environmental conditions. Inferences of mutual interests may be based on behavioral information. Explanations for the suggested matches may be delivered to users, including behavioral information on which the suggested matches are based.

Abstract: A people matching system and method matches people in computer-implemented communities in which users subscribe to information generated by other users. The computer-generated suggested matches may be based on inferences of mutual interests, as well as on factors such as proximity and environmental conditions. The inferences of mutual interest may be based on behaviors such as subscriptions and physiological responses. Explanations for the suggested matches may be delivered to users.

Abstract: A people matching system and method matches people in response to a search request. The computer-generated suggested matches may be based on inferences of mutual interests, as well as on factors such as proximity and environmental conditions. The inferences of mutual interest may be based on behaviors such as subscriptions and physiological responses. Explanations for the suggested matches may be delivered to users, including behavioral information on which the suggested matches are based.

Abstract: A system and method for context-sensitive, adaptive commerce is described that features recommendations of products and/or services based on system usage behaviors and supplier contextual information such as inventory levels. Adaptive commerce may include the bundling of a plurality of products and generating an associated price based, at least in part, on usage behavior information. Other contextual information related to the customer or supplier may also be applied in generating recommended products, bundles of products, product configurations, and corresponding prices.

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 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 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 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 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 system and method for adaptive commerce is disclosed. Adaptive commerce enables recommendations of products or services based on usage behaviors and commercial contextual information. Commercial contextual information may include the business environment of the recommendation recipient, purchase histories, and product or service attributes. Bundles of products and/or services, or specific product or service configurations may be recommended. Corresponding prices may be determined in accordance with behavioral inferences and commercial contextual information.