Temporal Augmented Graph Neural Networks for Session-Based Recommendations

Huachi Zhou, Qiaoyu Tan, Xiao Huang (Corresponding Author), Kaixiong Zhou, Xiaoling Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Session-based recommendation aims to predict the next item that is most likely to be clicked by an anonymous user, based on his/her clicking sequence within one visit. It becomes an essential function of many recommender systems since it protects privacy. However, as the accumulated session records keep increasing, it becomes challenging to model the user interests since they would drift when the time span is large. Efforts have been devoted to handling dynamic user interests by modeling all historical sessions at one time or conducting offline retraining regularly. These solutions are far from practical requirements in terms of efficiency and capturing timely user interests. To this end, we propose a memory-efficient framework - TASRec. It constructs a graph for each day to model the relations among items. Thus, the same item on different days could have different neighbors, corresponding to the drifting user interests. We design a tailored graph neural network to embed this dynamic graph of items and learn temporal augmented item representations. Based on this, we leverage a sequential neural architecture to predict the next item of a given sequence. Experiments on real-world datasets demonstrate that TASRec outperforms state-of-the-art session-based recommendation methods.
Original languageEnglish
Title of host publication44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages1-5
Number of pages5
ISBN (Electronic)9781450380379
DOIs
Publication statusPublished - 11 Jul 2021
Event44th International ACM SIGIR Conference on Research and Development in Information Retrieval - , Canada
Duration: 11 Jul 202115 Jul 2021

Publication series

NameProceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Abbreviated titleSIGIR
Country/TerritoryCanada
Period11/07/2115/07/21

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