Self-Supervised Dual-Channel Attentive Network for Session-based Social Recommendation

Wang, Liuyin; Xu, Xianghong; Ouyang, Kai; Duan, Huanzhong; Lu, Yunfeng; Zheng, Haitao

doi:10.1109/icde53745.2022.00198

Cited by 10 publications

(1 citation statement)

References 27 publications

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“…S 2 -DHCN [35] builds dual channels to establish self-supervised learning [9] framework to learn session representations. Moreover, some advanced techniques are introduced into session-based scenarios for recommendations [34,26,16,15,36], refer to [40] for more details.…”

Section: Deep Neural Network Modelsmentioning

confidence: 99%

Rethinking Temporal Information in Session-Based Recommendation: A Position-Agnostic Approach

Xu,

Ouyang,

Zou

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Session-based Recommendation (SBR) aims to predict the next item for a session, which consists of several clicked items in a transaction. Most SBR approaches follow an underlying assumption that all sequential information should be strictly utilized. Thus, they model temporal information for items using implicit, explicit, or ensemble methods. In fact, users may recall previously clicked items but might not remember the exact order in which they were clicked. Therefore, focusing on representing item temporal information in various ways could make learning session intents challenging. In this paper, we rethink the necessity of temporal information for items in SBR. We propose Aggregating the Contextual intents of the session with Attentive networks, namely ACARec. Specifically, we avoid explicitly modeling positional embeddings and learn contextual intents through aggregation methods (convolutions or poolings). We also demonstrate that even an entirely position-agnostic aggregation approach can yield promising results. Extensive experiments on real-world datasets validate our arguments. We hope our study can provide insights into SBR and inspire future research in the community.

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