Event-Based Content Selection Using Calendar Parsing

The system performs preliminary action by accessing calendar events before they occur and using them to determine relevant content. The event parsing module extracts information from calendar events in advance, and the relevancy score generation module pre-calculates scores for keyword clusters based on these events, enabling content to be selected that is relevant to future user activities rather than relying solely on past browsing behavior.

The system introduces an intermediary mechanism by using calendar events as a bridge between user information and content selection. The event parsing module acts as an intermediary that translates calendar event data into structured event parameters, which then inform the keyword cluster selection and content relevance determination, filling the gap between browsing history and future event relevance.

The system applies segmentation by dividing the content selection process into distinct modular components: an event parsing module that extracts calendar information, an event parameters determination module that structures the data, a keyword cluster determination module that identifies relevant topics, and a relevancy score generation module that ranks content. This modular architecture manages complexity by separating concerns while maintaining event-based content relevance.

The system uses intermediary modules to manage complexity. The event parsing module serves as an intermediary between the calendar system and the content selection logic. The event parameters determination module acts as another intermediary layer that translates raw calendar data into structured parameters that can be used by subsequent modules, thereby simplifying the overall system architecture while enabling sophisticated event-based content selection.

The system performs preliminary action by pre-generating relevancy scores for multiple keyword clusters across different time periods relative to events. The relevancy score generation module creates these scores in advance based on event parameters, so that when content needs to be served, the system can quickly retrieve and use the pre-calculated scores rather than computing them in real-time, thereby maintaining timing accuracy while reducing processing burden during content delivery.

The system applies parameter changes by varying the time period parameter to generate different relevancy scores for the same keyword cluster at different stages relative to an event. For example, a keyword cluster may have high relevancy 3 days before an event but low relevancy 3 days after the event. This dynamic parameter adjustment enables precise content timing while systematically managing the data processing requirements through structured score generation.