Abstract: An automated pattern template generation system is provided. The system includes a memory having computer-readable instructions stored therein and a processor. The processor is configured to access a plurality of messages and process each of the plurality of messages to identify presence of string identifiers and numeric identifiers present in each of the plurality of messages. The processor is further configured to replace an identified numeric identifier in each message by a numeric label to generate a candidate template corresponding to the message. Furthermore, the processor is configured to merge the generated candidate templates based on pre-determined merging rules to replace one or more identified string identifiers with pre-determined string labels to generate the pattern templates. Each of the generated pattern templates includes numeric labels, string labels, or combinations thereof.

Abstract: An automated pattern template generation system is provided. The system includes a memory having computer-readable instructions stored therein and a processor. The processor is configured to access a plurality of messages and process each of the plurality of messages to identify presence of string identifiers and numeric identifiers present in each of the plurality of messages. The processor is further configured to replace an identified numeric identifier in each message by a numeric label to generate a candidate template corresponding to the message. Furthermore, the processor is configured to merge the generated candidate templates based on pre-determined merging rules to replace one or more identified string identifiers with pre-determined string labels to generate the pattern templates. Each of the generated pattern templates includes numeric labels, string labels, or combinations thereof.

Abstract: A multi-window time-aware personalized recommendation system and method is provided. The system includes a memory having computer-readable instructions stored therein. The system further includes a processor configured to access purchase history of one or more users and to select a pair of items purchased by the one or more users based on the purchase history. The processor is further configured to determine a contributory affinity value between a first item and a second item of the selected pair of items for each of the one or more users. The contributory affinity value is determined based on a first time-decay function and a second time-decay function associated with purchase times of the first and second items. In addition, the processor is configured to combine the contributory affinity values for each of the one or more users to estimate the affinity value between the first and second item across multiple time-windows.