A User Purchase Motivation-Aware Product Recommender System

Product recommender systems play a critical role in predicting users’ future interests based on their historical behavior, but meeting users’ all-time needs requires a deep understanding of their essential purchase motivations. However, this realm is dominated by methods that do not explicitly specify motivation categories or suffer from low data efficiency due to heavy reliance on extensive auxiliary data. To address this gap, we introduce a comprehensive framework for categorizing product purchase motivations. Building on this framework, we identify two key purchase motivations rooted in users’ inherent interests that drive purchasing decisions: stable preference and exploratory intent. Methodologically, we introduce a measure $\text{STB}$ to explicitly identify which of the two motivations drives the purchase of a product item, relying solely on historical behavior sequences and items’ intrinsic attributes. Leveraging this measure, we propose User PurchaSe moTivation-Aware Recommendation, a novel product recommendation method that establishes a clear inference chain for these two motivations, thereby enhancing overall recommendation performance. Extensive experiments on three real-world e-commerce recommendation scenarios demonstrate our model’s superiority over state-of-the-art recommendation benchmarks. Further empirical analyses offer intriguing insights and highlight the substantial progress our method achieves in tackling the challenging task of recommending items driven by exploratory intent.

History: Gautam Pant, Senior Editor; Jingjing Zhang, Associate Editor.

Funding: J. Xu and J. Wang received financial support from the National Natural Science Foundation of China [Grants 622006056 and 72442011]. T. Lu was not supported by any of the above funding sources.

Supplemental Materials: The online appendices are available at https://doi.org/10.1287/isre.2024.1028.