Text Rule Matching for Selective Information Recommendation

The user base is segmented into different groups based on their characteristics and preferences. Instead of treating all users uniformly, the system divides them into segments (e.g., female users, male users, or other demographic groups) and applies targeted recommendation strategies to each segment. This allows the system to recommend information to specific groups without unnecessarily processing recommendations for all users, thereby reducing overall resource consumption while maintaining appropriate coverage.

The recommendation system applies different quality levels of processing to different user segments. For users who match the target demographic (e.g., female users for a skirt advertisement), the system applies full recommendation processing. For users who do not match (e.g., male users), the system either skips recommendation or applies minimal processing. This local differentiation of processing quality reduces overall resource consumption while maintaining effective coverage for the target audience.

The system performs preliminary actions by pre-segmenting users into different groups based on their characteristics before the actual recommendation process. User profiles are pre-analyzed and categorized (e.g., identifying female users, students, white-collar workers) in advance. When a recommendation needs to be made, the system can quickly identify the target segment without performing complex real-time analysis for each user, thereby improving recommendation efficiency while maintaining broad reach.

The system applies partial action by recommending information only to the necessary subset of users who match the target criteria, rather than performing full recommendation processing for all users. For example, when promoting a skirt, the system applies recommendation actions only to female users and skips male users entirely. This partial application of the recommendation action maintains effective reach to the target audience while significantly improving efficiency by avoiding unnecessary processing.