@inproceedings{ouyang-etal-2022-social,
    title = "Social-aware Sparse Attention Network for Session-based Social Recommendation",
    author = "Ouyang, Kai  and
      Xu, Xianghong  and
      Tang, Chen  and
      Chen, Wang  and
      Zheng, Haitao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.159",
    doi = "10.18653/v1/2022.findings-emnlp.159",
    pages = "2173--2183",
    abstract = "Session-based Social Recommendation (SSR) aims to use users{'} social networks and historical sessions to provide more personalized recommendations for the current session. Unfortunately, existing SSR methods have two limitations. First, they do not screen users{'} useless social relationships and noisy irrelevant interactions. However, user preferences are mainly affected by several close friends and key interactions. Second, when modeling the current session, they do not take full advantage of user preference information. To tackle these issues, we propose a novel Social-aware Sparse Attention Network for SSR, abbreviated as SSAN.It mainly consists of the Heterogeneous Graph Embedding (HGE) module and the Social-aware Encoder-decoder Network (SEN) module. In the HGE module, we adopt a modified heterogeneous graph neural network, which focuses more on close friends and key historical interactions, to enhance user/item representations. In the SEN module, we use the user representation as a bridge between the Encoder and Decoder to incorporate user preferences when modeling the current session. Extensive experiments on two benchmark datasets demonstrate the superiority of SSAN over the state-of-the-art models.",
}