Continuous Exp Strategy for Consumer Preference Analysis based on Online Ratings

Understanding consumer preference for products or services is important for users (individuals, platforms, merchants, and so forth) to make decisions. However, the preference is difficult to observe. Based on the online ratings of consumers, we convert the ratings into pairwise comparisons and present an online optimization model to derive the ranking orders of the products or services. We employ a Continuous Exp strategy to develop a learning algorithm to solve the online optimization problem, which has almost the same performance as the best strategy ex-post. This approach can not only handle dynamic rating information with arbitrary rating distribution but is also efficient in computation. We also investigate the impact of the learning rate on the ranking order and provide a real-world application of a recommendation system for illustration.