Attribute-driven Interest Modeling for Sequential Recommendation

Qiang DING, Tianhao SUN, Mingliang ZHOU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


User interest modeling in sequential recommendation is to learn the underlying interests from the user's historical interaction records, and then predict a list of items that the user might interact with. Existing studies treat each item as an integral unit, and mainly focus on extracting useful information from the item sequence directly. However, they neglect the fact that a user usually interacts with an item because of some attributes of it, and the cumulative result of attributes reflects the user's more specific interests. In this work, we propose a novel approach named Attribute-driven Interest Modeling for sequential recommendation (AIM), and explore user latent interests in a fine-grained manner. More specifically, our attribute-driven module captures more accurate interests from the attribute sequence, where each attribute first aggregates information from its neighbor items with a graph convolutional network. Then, the learned multiple interests are aggregated with the base interest extracted from the original item sequence to form user representation. Finally, the user representation is used to calculate the ranking score and make predictions. Extensive experiments on three real-world datasets show that our model outperforms the state-of-the-art baseline by 0.49% sim 8.61%.

Original languageEnglish
Title of host publication2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
Number of pages8
ISBN (Electronic)9781728186719
Publication statusPublished - 2022
Externally publishedYes
Event2022 International Joint Conference on Neural Networks, IJCNN 2022 - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2022 International Joint Conference on Neural Networks, IJCNN 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • attribute sequence
  • interest modeling
  • recommender system


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