Abstract: Described herein are various technologies pertaining to reconciling financial transaction information. A system for reconciling financial transactions can utilize incremental processing. Normalized financial data can be processed with submitted financial data identifiable from data derived from submitted financial data. A pluggable framework can utilize payment provider specific configuration data, a plugin model that provides information regarding a syntactic structure of financial data, and a plugin reader that provides semantic information regarding financial data. A distributed computation platform can utilize distributed computational units to utilize dynamic programming to derive a balance for particular accounts. Results from the distributed computational units are reduced into a point-in-time report. A first data version and a second data version from a particular payment provider can be retrieved. These data versions can be simultaneously maintained and accessible by a data consumer.

Abstract: Described herein are various technologies pertaining to reconciling financial transaction information. A system for reconciling financial transactions can utilize incremental processing. Normalized financial data can be processed with submitted financial data identifiable from data derived from submitted financial data. A pluggable framework can utilize payment provider specific configuration data, a plugin model that provides information regarding a syntactic structure of financial data, and a plugin reader that provides semantic information regarding financial data. A distributed computation platform can utilize distributed computational units to utilize dynamic programming to derive a balance for particular accounts. Results from the distributed computational units are reduced into a point-in-time report. A first data version and a second data version from a particular payment provider can be retrieved. These data versions can be simultaneously maintained and accessible by a data consumer.

Abstract: Systems and methods for personalized media recommendation are provided. One example system may include a media server including a media database containing a plurality of media tracks and a database schema defining multiple media styles. The media server may further include a media delivery engine configured to transmit media tracks to each of a plurality of media clients. The system may further include a feedback module configured to receive history data from each of the media clients. Further still, the system may include a recommendation engine configured to compute a plurality of user-specific style weights, and to select one or more recommended media tracks for each user, The recommendation engine may be further configured to send a recommendation message to the media client including media links to the recommended media tracks on the media server, to cause the media client to display the media links to a user.

Abstract: Systems and methods for personalized media recommendation are provided. One example system may include a media server including a media database containing a plurality of media tracks and a database schema defining multiple media styles. The media server may further include a media delivery engine configured to transmit media tracks to each of a plurality of media clients. The system may further include a feedback module configured to receive history data from each of the media clients. Further still, the system may include a recommendation engine configured to compute a plurality of user-specific style weights, and to select one or more recommended media tracks for each user, The recommendation engine may be further configured to send a recommendation message to the media client including media links to the recommended media tracks on the media server, to cause the media client to display the media links to a user.