Papers › TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

6 May 2020arXiv:2005.02844archive 2025-07-28

Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

Session-based recommendation nowadays plays a vital role in many websites, which aims to predict users' actions based on anonymous sessions. There have emerged many studies that model a session as a sequence or a graph via investigating temporal transitions of items in a session. However, these methods compress a session into one fixed representation vector without considering the target items to be predicted. The fixed vector will restrict the representation ability of the recommender model, considering the diversity of target items and users' interests. In this paper, we propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model. Moreover, TAGNN harnesses the power of graph neural networks to capture rich item transitions in sessions. Comprehensive experiments conducted on real-world datasets demonstrate its superiority over state-of-the-art methods.

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Tasks

DiversityGraph Neural NetworkSession-Based Recommendations

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Session-Based Recommendations Diginetica TAGNN Hit@20 51.31 #7 of 13 Archive leaderboard report
Session-Based Recommendations Diginetica TAGNN MRR@20 18.03 #7 of 13 Archive leaderboard report
Session-Based Recommendations yoochoose1 TAGNN MRR@20 31.12 #3 of 4 Archive leaderboard report
Session-Based Recommendations yoochoose1 TAGNN Precision@20 71.02 #3 of 4 Archive leaderboard report
Session-Based Recommendations yoochoose1/64 TAGNN HR@20 71.02 #8 of 11 Archive leaderboard report
Session-Based Recommendations yoochoose1/64 TAGNN MRR@20 31.12 #8 of 11 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Graph Neural Network

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