Abstract: A system includes instructions for execution by at least one processor, including, in response to an event, obtaining a first set of alerts stored in the alert database corresponding to a first scenario of a set of scenarios and selecting a first model of a set of models corresponding to the first scenario and identifying a first set of features stored in the features database corresponding to the first scenario. The instructions include, for each alert of the first set of alerts, identifying a first identifier included in the alert, retrieving the first set of features of the first identifier from the parameter database, determining a score using the first model based on the retrieved first set of features, and adding the alert and the score to a result list. The instructions include displaying, on a user device, the result list including the first set of alerts and corresponding scores.

Abstract: A recommendation system includes a content similarity analyzer configured to determine a first set of content item identifiers similar to a set of viewed content items based on respective similarity scores and add them to a first list. A similar user content extraction module identifies a set of similar user identifiers from a user similarity database; obtains, based on respective viewing histories of the set, a second set of content item identifiers; and adds them to the first list. The recommendation system includes a content filter configured to select a subset of content item identifiers from the first list based on the corresponding similarity scores between content item identifiers of the first list and a viewing history. The content filter is configured to transmit the subset of content item identifiers for display of the corresponding content items via a web portal on a user interface of a first user device.

Abstract: A content recommendation system includes a processor and a memory coupled to the processor. The memory stores instructions that, upon execution, cause the processor to obtain a first viewing history of a first user from the viewing history index. The instructions include determining, based on the user similarity index, a first set of users similar to the first user. The instructions include obtaining a corresponding viewing history from the viewing history index and selecting a set of similar content item identifiers based on similarity scores. The instructions include updating a first recommendation list with (i) the corresponding viewing history for each similar user in the first set of users and (ii) the set of similar content item identifiers. The instructions include selecting and transmitting to a user device a subset of recommended content item identifiers from the first recommendation list.

Abstract: A content recommendation system includes a processor and a memory coupled to the processor. The memory stores instructions that, upon execution, causes the processor to obtain a first viewing history of a first user from the viewing history index. The instructions include determining, based on the user similarity index, a first set of users similar to the first user. The instructions include obtaining a corresponding viewing history from the viewing history index and selecting a set of similar content item identifiers from the plurality of content item identifiers based on similarity scores. The instructions include updating a first recommendation list with (i) the corresponding viewing history for each similar user in the first set of users and (ii) the set of similar content item identifiers. The instructions include selecting and transmitting to a user device a subset of recommended content item identifiers from the first recommendation list.