Lycos, Inc.

Other

Donald M Kosak

Andrew K Lang

Michael John Witbrock

A method includes scanning content of a web page in which a web component is to be inserted, inferring a profile from the scanned content and serving the web component in the web page according to the profile.

Serving content to a client

A search engine system is provided for a portal site on the internet. The search engine system employs a regular search engine to make one-shot or demand searches for information entities which provide at least threshold matches to user queries. The search engine system also employs a collaborative/content-based filter to make continuing searches for information entities which match existing wire queries and are ranked and stored over time in user-accessible, system wires corresponding to the respective queries. A user feedback system provides collaborative feedback data for integration with content profile data in the operation of the collaborative/content-based filter. A query processor determines whether a demand search or a wire search is made for an input query.

Information filter system and method for integrated content-based and collaborative/adaptive feedback queries

Collaborative/adaptive search engine

An information processing system employs an integrated collaborative/content-based information filter structure to find network informons relevant to information desired by an individual user. The filter structure includes a three-level content-based filter which preprocesses informons in a first level to pass only relevant informons, applies community filters in a second level to pass relevant informons to matching communities of users, and applies in a bottom level user filters within each community to pass relevant informons to matching users. Collaborative filtering is integrated with content-based filtering through agent mind melding by which content profile data used in the content-based filtering is formed from a merger of an individual-user or member client data profile and any similar data profiles of other users or other member clients. A threshold level of similarity is required an other-user data profile to be classified as similar for merger with the individual-user data profile.

Integrated collaborative/content-based filter structure employing selectively shared, content-based profile data to evaluate information entities in a massive information network

A mindpool system develops collaboration informon rating data for use in filtering informons for a current user of a filter system. A plurality of commonly characterized user nodes are arranged in a 3-level heirarchical structure. Users input informon rating data to the bottom nodes. A mindpool manager system routes rating data received for an informon in any one user node to other user nodes in any node level according to selected criteria. This system distributes the rating data to selected users through bottom level nodes for use in filtering operations subsequently performed for the selected users. A common user characteristic applied to user nodes is increasingly generalized with upward node location and is increasingly detailed with downward node location. A mindpool manager operates for each user node to determine routings of the informon rating data to other user nodes. The mindpool system has a plurality of the heirarchical user node structures, with each operated for a specific informon rating purpose, such as quality. The mindpool is embodied in a filter structure having a content-based filter containing content profile data for an individual user and generating informon profile data representing information in a network sourced informon. The filter structure integrates the operation of the mindpool system and the content-based filter by combining the informon profile data, the content profile data, and received collaboration user rating data corresponding to the network informon to determine a value of the network informon to the individual user.

Multi-level mindpool system especially adapted to provide collaborative

An information entity rating system includes a content subsystem having a structured data sub-subsystem and an unstructured data sub-subsystem. The content subsystem receives content-based profile data for an information entity and separately processes structured and unstructured data to combine content-based profile data for an individual system user with the content-based profile data for the information entity to determine computed rating functions indicating structured and unstructured content-based value of the information entity to the user. A collaboration subsystem receives collaborative input data for the information entity and for processes the collaborative input data to determine at least one computed collaborative rating function indicating a collaboration-based value of the information entity to the user. A correlation subsystem receives data from the content subsystem and from the collaboration subsystem to determine exceptions to the computed rating functions on the basis of comparisons of data included in the content-based and collaboration data and to generate an exception data value function indicating an opposing value to at least one of the content-based and collaboration values. An output system combines the structured content-based. unstructured content-based, and collaboration-based value functions, and the exception data value function in generating an output rating predictor of the informon for consideration by the user.

System and method employing individual user content-based data and user

An apparatus, method, and computer program product for information filtering in a computer system receiving a data stream from a computer network, the data stream having raw informons embedded therein, at least one of the raw informons being of interest to a user, the user being a member client of a community. The method includes the steps of providing a dynamic informon characterization having profiles encoded therein, including an adaptive content profile and an adaptive collaboration profile; adaptively filtering the raw informons responsive to the dynamic informon characterization, and producing a proposed informon; presenting the proposed informon to the user; receiving a feedback profile from the user, responsive to the proposed informon; adapting the adaptive content profile, the adaptive collaboration profile, or both responsive to the feedback profile; and updating the dynamic informon characterization responsive to the previous adapting step. The apparatus includes a plurality of processors for providing interactive, distributed filtering of information, extracted from a computer network data stream in response to multiple attribute profiles.

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Sudbury, MA, United States of America

Donald M Kosak