Abstract:
A data model represents semantic information associated with objects stored in a file system. The data model includes a first object identifier, a second object identifier and a relation identifier. The first object identifier identifies a first object stored in the file system. The second object identifier identifies a second object stored in the file system, wherein the second object is related to the first object. The relation identifier identifies a relationship between the first object and the second object.
Abstract:
An apparatus has a server connected to a network, the server including a processor coupled to at least one data repository and software executing on the processor from a non-transitory medium, the software providing a service comprising receipt of a transaction record related to a transaction between a business and a customer, the record in the form of a digital file generated at a location of the business, processing of the transaction record, determining contextual meaning of individual portions of the record, inserting into the record an executable link on or proximate a portion processed for contextual meaning, the link to a resource associated with the contextual meaning of the portion linked, and transmitting the record with the inserted link to the customer.
Abstract:
The present invention is directed towards systems and methods for trust propagation. The method according to one embodiment comprises calculating a first feature vector for a first user, calculating a second feature for a second user and comparing the first feature vector with the second feature vector to calculate a similarity value. A determination is made as to whether the similarity value falls within a threshold. If the similarity value falls within the threshold, a relationship is recorded between the first user and the second user in a first user profile and a second user profile.
Abstract:
Techniques are provided for selecting which videos to recommend to users by predicting the degree to which recommending each video will satisfy certain goals. To make the predictions, a trained machine learning engine is fed both collaborative filtering parameter values and content-based filtering parameter values. In the case of video-to-video recommendations, the collaborative filtering parameter values may be based on a video pair that includes a video in which a user has already demonstrated an interest. The machine learning engine generates a machine-learning score for each video. The machine learning scores are used as the basis for selecting which videos to recommend to a particular user.
Abstract:
The present invention relates to systems, methods, and user interfaces for browsing a collection of content items saved by a user or by one or more buddies associated with a given user. The method of the present invention comprises saving one or more content items and one or more associated keywords as specified by a user. An interface is generated that displays the one or more saved content items and the one or more associated keywords, as well as the one or more buddies associated with a given user. A user indication of the selection of a given keyword or the selection of a given buddy by the user is received. The one or more displayed content items are filtered according to the selected keyword, buddy, or combination of selected keyword and buddy.
Abstract:
The present invention relates to systems, methods, and user interfaces for browsing a collection of content items saved by a user or by one or more buddies associated with a given user. The method of the present invention comprises saving one or more content items and one or more associated keywords as specified by a user. An interface is generated that displays the one or more saved content items and the one or more associated keywords, as well as the one or more buddies associated with a given user. A user indication of the selection of a given keyword or the selection of a given buddy by the user is received. The one or more displayed content items are filtered according to the selected keyword, buddy, or combination of selected keyword and buddy.
Abstract:
A query-centric system and process for distributing reverse indices for a distributed content system. Relevance ranking techniques in organizing distributed system indices. Query-centric configuration subprocesses (1) analyze query data, partitioning terms for reverse index server(s) (RIS), (2) distribute each partitioned data set by generally localizing search terms for the RIS that have some query-centric correlation, and (3) generate and maintain a map for the partitioned reverse index system terms by mapping the terms for the reverse index to a plurality of different index server nodes. Indexing subprocess element builds distributed reverse indices from content host indices. Routines of the query execution use the map derived in the configuration to more efficiently return more relevant search results to the searcher.
Abstract:
An overlay network is used to logically represent an underlying physical network. A map associated with a region of the overlay network includes location information for nodes physically close in the physical network. The map is used to select a closest node in the physical network.
Abstract:
A multicast tree is provided in an application multicast network. A child node in the multicast tree detects a degradation of quality of service associated with a service being received at the child node. The child node determines whether the degradation of quality of service is resulting from a child-parent link or an upstream link in the multicast tree.
Abstract:
In a method of generating a routing table for a selected peer, a zone of the selected peer is compared to a target zone. A current entry associated with the zone of the selected peer is created in a routing table of the selected peer in response to the zone of the selected peer being one of smaller and equal to the target zone.