{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:26:27Z","timestamp":1787239587541,"version":"3.56.0"},"reference-count":72,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"Public Computing Cloud of Renmin University of China"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Social recommendation has been proven effective in addressing data sparsity in user\u2013item interaction modeling by leveraging social networks. The recent integration of Graph Neural Networks (GNNs) has further enhanced prediction accuracy in contemporary social recommendation algorithms. However, many GNN-based approaches in social recommendation lack the ability to furnish meaningful explanations for their predictions. In this study, we confront this challenge by introducing SoREX, a self-explanatory GNN-based social recommendation framework. SoREX adopts a two-tower framework enhanced by friend recommendation, independently modeling social relations and user\u2013item interactions, while jointly optimizing an auxiliary task to reinforce social signals. To offer explanations, we propose a novel ego-path extraction approach. This method involves transforming the ego-net of a target user into a collection of multi-hop ego-paths, from which we extract factor-specific and candidate-aware ego-path subsets as explanations. This process facilitates the summarization of detailed comparative explanations among different candidate items through intricate substructure analysis. Furthermore, we conduct explanation re-aggregation to explicitly correlate explanations with downstream predictions, imbuing our framework with inherent self-explainability. Comprehensive experiments conducted on four widely adopted benchmark datasets validate the effectiveness of SoREX in predictive accuracy. Additionally, qualitative and quantitative analyses confirm the effectiveness of the explanations extracted by SoREX. The corresponding code and data are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/antman9914\/SoREX\">https:\/\/github.com\/antman9914\/SoREX<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3777374","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T14:13:52Z","timestamp":1763388832000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0951-8890","authenticated-orcid":false,"given":"Hanze","family":"Guo","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1608-580X","authenticated-orcid":false,"given":"Yijun","family":"Ma","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0868-764X","authenticated-orcid":false,"given":"Xiao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186070"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.55.090902.142015"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482306"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3689647"},{"key":"e_1_3_2_6_2","unstructured":"Jeffrey Dastin. 2018. Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women. Reuters. Retrieved July 1 2025 from https:\/\/www.reuters.com\/article\/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583303"},{"key":"e_1_3_2_8_2","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS \u201922)","author":"Fan Shaohua","year":"2024","unstructured":"Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, and Jian Tang. 2024. Debiasing graph neural networks via learning disentangled causal substructure. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS \u201922). Article 1808, 13 pages."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_10_2","volume-title":"Proceedings of the 7th International Conference on Learning Representations (RLGM Workshop) (ICLR \u201918)","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch geometric. In Proceedings of the 7th International Conference on Learning Representations (RLGM Workshop) (ICLR \u201918)."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2528249"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963772"},{"key":"e_1_3_2_14_2","unstructured":"Alex Hern. 2017. YouTube and Google Boycott Spreads to US as AT&T and Verizon Pull Ads. The Guardian. Retrieved July 1 2025 from https:\/\/www.theguardian.com\/technology\/2017\/mar\/23\/youtube-google-boycott-att-verizon-pull-adverts-extremism"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614806"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i11.29157"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864736"},{"key":"e_1_3_2_18_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Jang Eric","year":"2016","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with Gumbel-Softmax. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.03.070"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645460"},{"key":"e_1_3_2_21_2","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657962"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28712"},{"key":"e_1_3_2_24_2","unstructured":"Lei Li Xiao Zhou and Zheng Liu. 2025. R2MED: A benchmark for reasoning-driven medical retrieval. arXiv:2505.14558. Retrieved from https:\/\/arxiv.org\/abs\/2505.14558"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080822"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679630"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.01.001"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497370"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/1935826.1935877"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28737"},{"key":"e_1_3_2_31_2","first-page":"15524","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao, Mia Liu, and Pan Li. 2022. Interpretable and generalizable graph learning via stochastic attention mechanism. In Proceedings of the International Conference on Machine Learning, 15524\u201315543."},{"key":"e_1_3_2_32_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao, Yunan Luo, Mia Liu, and Pan Li. 2022. Interpretable geometric deep learning via learnable randomness injection. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_2_33_2","volume-title":"Proceedings of the 12th International Conference on Learning Representations (ICLR \u201924)","author":"M\u00fcller Peter","year":"2024","unstructured":"Peter M\u00fcller, Lukas Faber, Karolis Martinkus, and Roger Wattenhofer. 2024. GraphChef: Decision-tree recipes to explain graph neural networks. In Proceedings of the 12th International Conference on Learning Representations (ICLR \u201924)."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450120"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_36_2","first-page":"76737","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Seo Sangwoo","year":"2023","unstructured":"Sangwoo Seo, Sungwon Kim, and Chanyoung Park. 2023. Interpretable prototype-based graph information bottleneck. In Proceedings of the 37th International Conference on Neural Information Processing Systems, 76737\u201376748."},{"key":"e_1_3_2_37_2","first-page":"22523","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS \u201921","volume":"34","author":"Shan Caihua","year":"2021","unstructured":"Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, and Dongsheng Li. 2021. Reinforcement learning enhanced explainer for graph neural networks. In Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS \u201921), Vol. 34, 22523\u201322533."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539192"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539366"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331244"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512003"},{"key":"e_1_3_2_42_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 30."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210010"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00074"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015329"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441744"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557583"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3048414"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331214"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2018.2872842"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331203"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512031"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441726"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2605085"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671807"},{"key":"e_1_3_2_56_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"32","author":"Yeh Chih-Kuan","year":"2019","unstructured":"Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I. Inouye, and Pradeep K. Ravikumar. 2019. On the (In)fidelity and sensitivity of explanations. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32."},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","unstructured":"Haoteng Yin Muhan Zhang Yanbang Wang Jianguo Wang and Pan Li. 2022. Algorithm and system co-design for efficient subgraph-based graph representation learning. Proceedings of the VLDB Endowment 15 11 (2022) 2788\u20132796.","DOI":"10.14778\/3551793.3551831"},{"key":"e_1_3_2_58_2","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems (NeurIPS \u201919)","volume":"32","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. GNNExplainer: Generating explanations for graph neural networks. In Proceedings of the 33rd International Conference on Neural Information Processing Systems (NeurIPS \u201919), Vol. 32."},{"key":"e_1_3_2_59_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Yu Junchi","year":"2020","unstructured":"Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He. 2020. Graph information bottleneck for subgraph recognition. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467340"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3033673"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449844"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507230"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403085"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599435"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000066"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609579"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20898"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401171"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570421"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330781"},{"issue":"7","key":"e_1_3_2_72_2","first-page":"4349","article-title":"Tricolore: Multi-behavior user profiling for enhanced candidate generation in recommender systems","volume":"37","author":"Zhou Xiao","year":"2025","unstructured":"Xiao Zhou, Zhongxiang Zhao, and Hanze Guo. 2025. Tricolore: Multi-behavior user profiling for enhanced candidate generation in recommender systems. IEEE Transactions on Knowledge and Data Engineering 37, 7 (2025), 4349\u20134360.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_73_2","unstructured":"Huaisheng Zhu Dongsheng Luo Xianfeng Tang Junjie Xu Hui Liu and Suhang Wang. 2023. Self-explainable graph neural networks for link prediction. arXiv:2305.12578. Retrieved from https:\/\/arxiv.org\/abs\/2305.12578"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3777374","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T14:06:22Z","timestamp":1766498782000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3777374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,23]]},"references-count":72,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2,28]]}},"alternative-id":["10.1145\/3777374"],"URL":"https:\/\/doi.org\/10.1145\/3777374","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,23]]},"assertion":[{"value":"2024-12-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}