Exploring consumer preferences is a crucial task for recommender systems to address the problems of popularity bias, filter bubbles, and consumer boredom, leading to significant improvements in consumer experiences as well as business performance. Meanwhile, when selecting the appropriate exploratory content for expanding consumers' horizons, existing approaches usually suffer from inadequate support of temporal dynamics and personalization in the exploration process, since they do not explicitly model the sequential evolution of preference of each consumer. In addition, they do not systematically capture the long-term impact of exploratory products on future user behaviors. To tackle these challenges, we propose a novel recommendation framework in this paper, where we formulate recommendations as a consumer trajectory learning task and determine trajectory points based on the concept of dynamical systems in mathematics. By doing so, we can simultaneously optimize both the magnitude and the trend of consumer exploration through the transition vectors connecting trajectory points, as well as capturing the sequential continuity and evolution of consumer interests. In addition, we design a novel forward-looking component into the dynamical system to model the impact of current consumer decisions on their future behaviors, leading to even more insightful and useful recommendations. We conduct extensive simulation and offline experiments to demonstrate significant improvements of our method over the state-of-the-art baselines in terms of both the exploration and exploitation performance. In addition, we conduct a large-scale online controlled experiment at a leading video streaming platform in Asia, where our method significantly outperforms the latest production model in the company across multiple business metrics and leads to better consumer experiences. These improvements potentially translate into an additional 20 million USD annual revenue for the company based on the estimate of the manager in charge of the described platform, demonstrating tangible economic impact.
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