Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence
Xuan Yang, James A. Esquivel
Abstract
Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence. Recently, deep neural network-based sequential recommendation models gained considerable attention. However, existing approaches pay little attention to users' dynamically evolving interests, which are influenced by product attributes, especially product category. To overcome these challenges, we propose a dynamic personalized recommendation model: DynaPR. Specifically, we first embed product information and attribute information into a unified data space. Then, we exploit long short-term memory (LSTM) networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM networks. Finally, similarity values between users are measured through pairwise interest features, and personalized recommendation lists are generated. A series of experiments reveal the superiority of the proposed method compared with other advanced methods.