System and method for searching and recommending documents in a collection using share bookmarks

A system for maintaining a collection of shared document content pointers contributed by a plurality of users, wherein each shared document content pointer represents a document in a document repository, comprising:

• a plurality of user terminals in communication with the repository;
• a database in communication with the repository and the plurality of user terminals, wherein the database is adapted to store the collection of shared document content pointers;
• and
• software adapted to maintain the collection of shared document content pointers.

A method for generating a recommendation from a collection of shared document content pointers contributed by a plurality of users, wherein each shared document content pointer represents a document, comprising the steps of:

• identifying at least one unavailable content pointer;
• searching at least a portion of the collection of shared document content pointers;
• and
• retrieving a group of content pointers that match the unavailable content pointer.

A method for searching an information repository comprising the steps of:

• establishing a collection of shared content pointers, the content pointers contributed by and shared among a plurality of users;
• receiving at least one subject keyword;
• searching at least a portion of the collection of shared content pointers;
• and
• retrieving a group of content pointers that match the subject keyword;
• identifying a context within the collection of shared content pointers;
• ranking the group of content pointers based a computed match with the context;
• and
• wherein the context comprises a profile for a group of users chosen from the plurality of users.

A method for searching an information repository comprising the steps of:

• establishing a collection of content pointers, the content pointers contributed by a plurality of users;
• receiving at least one query keyword;
• augmenting the query with at least one additional keyword derived from the collection;
• searching the information repository;
• and
• retrieving at least one of a group of documents, each including content indicia, at least one content index matching the query keyword.

A method for searching an information repository comprising the steps of:

• establishing a collection of content pointers, the content pointers contributed by a plurality of users;
• receiving at least one query keyword;
• searching the information repository;
• identifying a context within the collection;
• retrieving at least one of a group of documents, each including content indicia, at least one content index matching the query keyword, wherein the retrieved at least one of a group of documents is ranked in accordance with a computed match with the context;
• and
• wherein the context comprises a profile, the profile selected from a group consisting of a user profile for a single user chosen from the plurality of users, a group profile for a group of users chosen from the plurality of users,
• and a profile for the plurality of users.

A method for searching an information repository comprising the steps of:

• establishing a collection of content pointers, the content pointers contributed by a plurality of users;
• receiving at least one query keyword;
• searching the information repository;
• retrieving at least one of a group of documents, each including content indicia, at least one content index matching the query keyword;
• and
• ordering the retrieved documents in a ranking order, the order determined by a weighted set of user metrics.

A method for generating ordered recommendations of content sources from an information repository comprising the steps of:

• establishing a collection of content pointers, the content pointers contributed by a plurality of users, each content pointer associated with a content source;
• receiving at least one query keyword;
• searching the information repository;
• retrieving a group of content sources that each includes a content profile matching the query keyword;
• identifying a context within the collection;
• and
• ranking the group of content sources based on a computed match between each content source's content profile and the identified context.

A method for generating ordered recommendations of content sources from an information repository comprising the steps of:

• establishing a collection of content pointers, the content pointers contributed by a plurality of users, each content pointer associated with a content source;
• receiving at least one query keyword;
• searching the information repository;
• retrieving a group of content sources that match the query keyword;
• identifying a context within the collection;
• identifying a set of user recommendation priority metrics;
• combining a computed match with the context and the set of user recommendation priority metrics for each content source into a composite recommendation metric;
• and
• ranking the group of content sources based on each content source's composite recommendation metric.

An information repository search engine comprising:

• a collection of content pointers, the content pointers contributed by a plurality of users;
• a user input engine, configured to receive at least a keyword query input;
• a query augmentation engine, configured to derive an associate keyword from the collection;
• and
• a search engine, configured to search the collection of shared content pointers.

In a search engine of the type configured to search an information repository on the basis of at least a query keyword input by a user, the search engine retrieving at least one of a group of content sources, each content source including content indicia, a recommendation engine comprising:

• a collection of content pointers, the content pointers contributed by a plurality of users;
• a metrics identification engine, the metrics identification engine identifying a set of user recommendation metrics;
• and
• an ordering engine, the ordering engine ordering a set of retrieved content sources in accordance with the set of user recommendation metrics.