Diversifying personalized recommendation with user-session context

Liang HU, Longbing CAO, Shoujin WANG, Guandong XU, Jian CAO, Zhiping GU

Research output: Chapter in Book/Report/Conference proceedingChapters

90 Citations (Scopus)


Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
Place of PublicationVienna, Austria
PublisherInternational Joint Conferences on Artificial Intelligence
ISBN (Electronic)9780999241103
Publication statusPublished - 2017


Hu, L., Cao, L., Wang, S., Xu, G., Cao, J., & Gu, Z. (2017). Diversifying personalized recommendation with user-session context. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) (pp. 1858-1864). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/258


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