Abstract:

AI-based personalized recommendation services are now widely used by companies in various marketing areas. Companies analyze consumers’ purchasing behavior using AI and understand their preferences through user profiling. It allows them to recommend better-match products to consumers in future sales periods. AI can improve matching accuracy between recommended products and consumers， stimulate consumption thus expanding the market and promoting sales. However， it also may lead strategic consumers to expect better product recommendations in the future， wait and delay their purchases， thus resulting profit loss. As a result， companies need to be more cautious using AI-based recommendations. How companies should strategically use AI-based personalized recommendation services is explored through commitment strategies. A two-period dynamic game model is used to explore two commitment strategies：（1）Price commitment strategy， where the company announces the product prices of two periods in advance， similar to real-world price protection strategies；（2）Enhanced-match commitment strategy， where companies promote their AI recommendations to ensure that consumers have accurate expectations about the enhanced-match precision of future recommended products. Commitment strategies influence consumers' value assessments of products across two sales periods， thereby regulating purchasing behavior and creating strategic interactions for AI-based recommendations. The results indicate that AI-based personalized recommendation service is not necessarily beneficial. Only when the originally existence of purchase delay is not severe or the sales-promotion effect is strong enough， can the adoption of AI benefit the firms. Interestingly， although Enhanced-match strategy may seem to encourage delayed purchases， it actually reduces this effect and requires only a modest improvement in matching accuracy. Additionally， the relationship between the AI use extent and the costs of enhanced-match accuracy is not necessarily positive. Companies may need to heavily promote AI-based recommendations to enhance product matching accuracy， even with high costs， depending on the trade-off between sales promotion and delayed purchase effects. When the cost of enhanced-match accuracy is relatively low， the Enhanced-match strategy plays better than traditional price commitment strategies. The consumer surplus and total social welfare are also compared under different strategies.

Key words: AI recommendation, commitment, matching accuracy, delayed purchase, game