Learning user’s dynamic and personalized interests from E-commerce reviews of verified purchases: a time series based framework

User interests are highly individualized and evolve over time. Nevertheless, the exploration of these personalized traits alongside the dynamics of such interests is less studied. Based on the observation that the sequences of a user’s reviews of verified purchases on a product can reflect the user’s dynamic interests toward the product type, we propose a time-series-based framework that elucidates both the evolutions and characteristics within user interests, learns the personalized patterns in interest series, and suggests three strategies to predict users’ prospective interests. The experiments used 1.29 million Amazon’s reviews of verified purchases on electronic products to validate the proposed framework. The results showed that a part of the users’ interest series did have unique life-cycles, and the proposed prediction strategies outperformed both traditional static method and recent deep sequential models in terms of precision, recall rate, and F1, which indicated that “personalization” and “time” indeed played critical roles in user interest analysis. The proposed approach can be used to enhance predictions regarding users’ prospective interests while mitigating information overload. This work holds potential for a variety of applications such as user profile construction, precision marketing, and personalized recommendation.