Advertisement Placement Strategies Based on Artificial Intelligence Hotspot Tracking Algorithms

The application of AI technology in advertisement placement strategy has become increasingly important. Thus, advertisement placement strategies were studied using AI hotspot tracking technology through in-depth analysis of consumer behavioral data and market trends. The results can be used for personalized customization and precise targeting of advertisement content. The effect of hotspot tracking algorithms was evaluated using the term frequency-inverse document frequency (TF-IDF) in predicting consumer preferences and optimizing advertisement placement strategies. TF-IDF is a text mining technique used to effectively extract keywords from a large amount of text data to hot topics of consumer interest. The outcome is used to generate advertising content. By applying the TF -IDF algorithm to consumer-generated content analysis, consumer interest and emotional tendencies toward specific topics are identified and quantified to predict market trends. Combining the results of AI hotspot tracking and TF -IDF analysis, a set of innovative ad delivery strategies was proposed to improve the relevance and attractiveness of advertisements and adjust the content and delivery time. Based on real-time consumer feedback and behavioral patterns, the strategies can be used to achieve a higher return on investment.