Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation

Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation

Guibing Guo, Shichang Ouyang, Xiaodong He, Fajie Yuan, Xiaohua Liu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1373-1379. https://doi.org/10.24963/ijcai.2019/190

Sequential recommendation systems have become a research hotpot recently to suggest users with the next item of interest (to interact with). However, existing approaches suffer from two limitations: (1) The representation of an item is relatively static and fixed for all users. We argue that even a same item should be represented distinctively with respect to different users and time steps. (2) The generation of a prediction for a user over an item is computed in a single scale (e.g., by their inner product), ignoring the nature of multi-scale user preferences. To resolve these issues, in this paper we propose two enhancing building blocks for sequential recommendation. Specifically, we devise a Dynamic Item Block (DIB) to learn dynamic item representation by aggregating the embeddings of those who rated the same item before that time step. Then, we come up with a Prediction Enhancing Block (PEB) to project user representation into multiple scales, based on which many predictions can be made and attentively aggregated for enhanced learning. Each prediction is generated by a softmax over a sampled itemset rather than the whole item space for efficiency. We conduct a series of experiments on four real datasets, and show that even a basic model can be greatly enhanced with the involvement of DIB and PEB in terms of ranking accuracy. The code and datasets can be obtained from https://github.com/ouououououou/DIB-PEB-Sequential-RS
Keywords:
Humans and AI: Personalization and User Modeling
Multidisciplinary Topics and Applications: Recommender Systems