Abstract: Techniques to extract data from computer-readable purchase records of a user, cluster the items of interest based on descriptions of the items, and associate descriptive keywords to the clusters, where the keywords represent interests of the user. One or more processes and/or functions may be performed on extracted data, including cluster-specific processes and/or function, including user-based, user interest-based, and/or crowd-based processes and/or function, which may include shopping pattern extraction, item or types of items availability based on time, location and other contextual metric, pricing data of items and expected pricing changes over time and seasonal variations, identification of user preferences, and/or shopping recommendations.

Abstract: Selective/controlled disclosure of user information to private workspaces of other users/invitees based on context/contextual relations, and a shared workspace or market to collaborate amongst the other users (e.g., to crowd-source gifts of interest to the recipient). Contextual disclosure may be based on common context or commonality under a set of conditions, such as a topic, which may include known topics of relationships amongst the users and/or undiscovered contexts. As an example, items of interest to each user are identified and clustered, keywords are assigned to the clusters indicative of topics/subjects of interests to the respective users, recipient keywords are compared to keywords of an invitee to identify common keywords as shared interests, and items of interest to the recipient that relate to the common keywords are disclosed to the invitee as a personalized wish-list. Keyword weighting and/or keyword/item level privacy designations may be provided to further control disclosure.

Abstract: Selective/controlled disclosure of user information to private workspaces of other users/invitees based on context/contextual relations, and a shared workspace or market to collaborate amongst the other users (e.g., to crowd-source gifts of interest to the recipient). Contextual disclosure may be based on common context or commonality under a set of conditions, such as a topic, which may include known topics of relationships amongst the users and/or undiscovered contexts. As an example, items of interest to each user are identified and clustered, keywords are assigned to the clusters indicative of topics/subjects of interests to the respective users, recipient keywords are compared to keywords of an invitee to identify common keywords as shared interests, and items of interest to the recipient that relate to the common keywords are disclosed to the invitee as a personalized wish-list. Keyword weighting and/or keyword/item level privacy designations may be provided to further control disclosure.

Abstract: Techniques to extract data from computer-readable purchase records of a user, cluster the items of interest based on descriptions of the items, and associate descriptive keywords to the clusters, where the keywords represent interests of the user. One or more processes and/or functions may be performed on extracted data, including cluster-specific processes and/or function, including user-based, user interest-based, and/or crowd-based processes and/or function, which may include shopping pattern extraction, item or types of items availability based on time, location and other contextual metric, pricing data of items and expected pricing changes over time and seasonal variations, identification of user preferences, and/or shopping recommendations.