{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:05:22Z","timestamp":1770336322704,"version":"3.49.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0902703"],"award-info":[{"award-number":["2022YFF0902703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0902703"],"award-info":[{"award-number":["2022YFF0902703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0902703"],"award-info":[{"award-number":["2022YFF0902703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0902703"],"award-info":[{"award-number":["2022YFF0902703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0902703"],"award-info":[{"award-number":["2022YFF0902703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11280-025-01397-1","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T05:22:45Z","timestamp":1767936165000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["STLLM-Rec: enhancing explainable recommendation via self-training LLMs"],"prefix":"10.1007","volume":"29","author":[{"given":"Ziyu","family":"Li","sequence":"first","affiliation":[]},{"given":"Zhijie","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Suhuan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Weiping","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Mo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"1397_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, C., Zhao, Z.: Lightweight yet efficient: An external attentive graph convolutional network with positional prompts for sequential recommendation. ACM Trans. Inform. Syst. (2025)","DOI":"10.1145\/3719343"},{"issue":"1","key":"1397_CR2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s11280-025-01332-4","volume":"28","author":"R Zhao","year":"2025","unstructured":"Zhao, R., Zhang, Y., Ju, S., Peng, J., Yang, Y.: Adaptive user multi-level and multi-interest preferences for sequential recommendation. World Wide Web. 28(1), 20 (2025)","journal-title":"World Wide Web."},{"issue":"6","key":"1397_CR3","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s11280-024-01295-y","volume":"27","author":"Y Ma","year":"2024","unstructured":"Ma, Y., Gan, M.: Sequential-hierarchical attention network: Exploring the hierarchical intention feature in poi recommendation. World Wide Web. 27(6), 67 (2024)","journal-title":"World Wide Web."},{"issue":"1","key":"1397_CR4","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s11280-024-01236-9","volume":"27","author":"S Ruan","year":"2024","unstructured":"Ruan, S., Yang, C., Li, D.: Knowledge-enhanced personalized hierarchical attention network for sequential recommendation. World Wide Web. 27(1), 2 (2024)","journal-title":"World Wide Web."},{"key":"1397_CR5","doi-asserted-by":"crossref","unstructured":"Chen, H., Shi, S., Li, Y., Zhang, Y.: Neural collaborative reasoning. In: Proceedings of the Web Conference 2021, pp. 1516\u20131527 (2021)","DOI":"10.1145\/3442381.3449973"},{"key":"1397_CR6","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639\u2013648 (2020)","DOI":"10.1145\/3397271.3401063"},{"issue":"2","key":"1397_CR7","first-page":"913","volume":"36","author":"J Yu","year":"2023","unstructured":"Yu, J., Xia, X., Chen, T., Cui, L., Hung, N.Q.V., Yin, H.: Xsimgcl: Towards extremely simple graph contrastive learning for recommendation. IEEE Trans. Knowl. Data Eng. 36(2), 913\u2013926 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"1397_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11280-025-01340-4","volume":"28","author":"M Moosazadeh","year":"2025","unstructured":"Moosazadeh, M., Kaedi, M.: Cct-gnn: Collaborative category and time-aware graph neural networks for session-based recommendation systems. World Wide Web. 28(3), 1\u201326 (2025)","journal-title":"World Wide Web."},{"key":"1397_CR9","doi-asserted-by":"crossref","unstructured":"Kang, W.-C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197\u2013206 (2018). IEEE","DOI":"10.1109\/ICDM.2018.00035"},{"key":"1397_CR10","doi-asserted-by":"crossref","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441\u20131450 (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"1397_CR11","doi-asserted-by":"crossref","unstructured":"Xie, X., Sun, F., Liu, Z., Wu, S., Gao, J., Zhang, J., Ding, B., Cui, B.: Contrastive learning for sequential recommendation. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 1259\u20131273 (2022). IEEE","DOI":"10.1109\/ICDE53745.2022.00099"},{"issue":"5","key":"1397_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11280-024-01291-2","volume":"27","author":"L Wu","year":"2024","unstructured":"Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., et al.: A survey on large language models for recommendation. World Wide Web. 27(5), 60 (2024)","journal-title":"World Wide Web."},{"key":"1397_CR13","doi-asserted-by":"crossref","unstructured":"Koa, K.J., Ma, Y., Ng, R., Chua, T.-S.: Learning to generate explainable stock predictions using self-reflective large language models. In: Proceedings of the ACM on Web Conference 2024, pp. 4304\u20134315 (2024)","DOI":"10.1145\/3589334.3645611"},{"key":"1397_CR14","doi-asserted-by":"crossref","unstructured":"Dong, L., Huang, S., Wei, F., Lapata, M., Zhou, M., Xu, K.: Learning to generate product reviews from attributes. In: 15th EACL 2017 Software Demonstrations, pp. 623\u2013632 (2017). Association for Computational Linguistics","DOI":"10.18653\/v1\/E17-1059"},{"key":"1397_CR15","doi-asserted-by":"crossref","unstructured":"Li, P., Wang, Z., Ren, Z., Bing, L., Lam, W.: Neural rating regression with abstractive tips generation for recommendation. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 345\u2013354 (2017)","DOI":"10.1145\/3077136.3080822"},{"key":"1397_CR16","doi-asserted-by":"crossref","unstructured":"Li, L., Zhang, Y., Chen, L.: Personalized transformer for explainable recommendation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021)","DOI":"10.18653\/v1\/2021.acl-long.383"},{"issue":"4","key":"1397_CR17","first-page":"1","volume":"41","author":"L Li","year":"2023","unstructured":"Li, L., Zhang, Y., Chen, L.: Personalized prompt learning for explainable recommendation. ACM Trans. Inform. Syst. 41(4), 1\u201326 (2023)","journal-title":"ACM Trans. Inform. Syst."},{"key":"1397_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, A., Chen, Y., Sheng, L., Wang, X., Chua, T.-S.: On generative agents in recommendation. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1807\u20131817 (2024)","DOI":"10.1145\/3626772.3657844"},{"key":"1397_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wu, J., Wang, X., Tang, W., Wang, D., De\u00a0Rijke, M.: Let me do it for you: Towards llm empowered recommendation via tool learning. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1796\u20131806 (2024)","DOI":"10.1145\/3626772.3657828"},{"key":"1397_CR20","doi-asserted-by":"crossref","unstructured":"Zheng, B., Hou, Y., Lu, H., Chen, Y., Zhao, W.X., Chen, M., Wen, J.-R.: Adapting large language models by integrating collaborative semantics for recommendation. In: 2024 IEEE 40th International Conference on Data Engineering (ICDE), pp. 1435\u20131448 (2024). IEEE","DOI":"10.1109\/ICDE60146.2024.00118"},{"key":"1397_CR21","doi-asserted-by":"crossref","unstructured":"Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., Zhao, W.X.: Large language models are zero-shot rankers for recommender systems. In: European Conference on Information Retrieval, pp. 364\u2013381 (2024). Springer","DOI":"10.1007\/978-3-031-56060-6_24"},{"key":"1397_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128904","volume":"616","author":"M-R Amini","year":"2025","unstructured":"Amini, M.-R., Feofanov, V., Pauletto, L., Hadjadj, L., Devijver, E., Maximov, Y.: Self-training: A survey. Neurocomputing 616, 128904 (2025)","journal-title":"Neurocomputing"},{"key":"1397_CR23","first-page":"55006","volume":"36","author":"C Zhou","year":"2023","unstructured":"Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al.: Lima: Less is more for alignment. Adv. Neural. Inf. Process. Syst. 36, 55006\u201355021 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1397_CR24","unstructured":"Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., Cobbe, K.: Let\u2019s verify step by step. In: The Twelfth International Conference on Learning Representations (2023)"},{"key":"1397_CR25","doi-asserted-by":"crossref","unstructured":"Bing, Q., Zhu, Q., Dou, Z.: Cognition-aware knowledge graph reasoning for explainable recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 402\u2013410 (2023)","DOI":"10.1145\/3539597.3570391"},{"issue":"7","key":"1397_CR26","doi-asserted-by":"publisher","first-page":"3443","DOI":"10.1109\/TKDE.2024.3354077","volume":"36","author":"X Wang","year":"2024","unstructured":"Wang, X., Li, Q., Yu, D., Li, Q., Xu, G.: Reinforced path reasoning for counterfactual explainable recommendation. IEEE Trans. Knowl. Data Eng. 36(7), 3443\u20133459 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"1397_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3672276","volume":"14","author":"MA Chatti","year":"2024","unstructured":"Chatti, M.A., Guesmi, M., Muslim, A.: Visualization for recommendation explainability: a survey and new perspectives. ACM Transactions on Interactive Intelligent Systems. 14(3), 1\u201340 (2024)","journal-title":"ACM Transactions on Interactive Intelligent Systems."},{"key":"1397_CR28","unstructured":"Li, G., Yang, H., Liu, X., Wu, Z., Dai, X.: Counterfactual language reasoning for explainable recommendation systems. arXiv preprint arXiv:2503.08051. (2025)"},{"key":"1397_CR29","doi-asserted-by":"crossref","unstructured":"Xian, Y., Fu, Z., De\u00a0Melo, G., Zhang, Y.: Reinforcement knowledge graph reasoning for explainable recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 285\u2013294 (2019)","DOI":"10.1145\/3331184.3331203"},{"key":"1397_CR30","doi-asserted-by":"crossref","unstructured":"Ma, Q., Ren, X., Huang, C.: Xrec: Large language models for explainable recommendation. In: Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 391\u2013402 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.22"},{"key":"1397_CR31","unstructured":"Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: React: Synergizing reasoning and acting in language models. In: International Conference on Learning Representations (ICLR) (2023)"},{"key":"1397_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, D., Li, S., Zhang, X., Zhan, J., Wang, P., Zhou, Y., Qiu, X.: Speechgpt: Empowering large language models with intrinsic cross-modal conversational abilities. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 15757\u201315773 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.1055"},{"issue":"7972","key":"1397_CR33","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172\u2013180 (2023)","journal-title":"Nature"},{"key":"1397_CR34","unstructured":"Cui, J., Li, Z., Yan, Y., Chen, B., Yuan, L.: Chatlaw: Open-source legal large language model with integrated external knowledge bases. CoRR. (2023)"},{"issue":"2","key":"1397_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3678004","volume":"43","author":"J Lin","year":"2025","unstructured":"Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., Liu, Y., Wu, C., Li, X., Zhu, C., et al.: How can recommender systems benefit from large language models: A survey. ACM Trans. Inform. Syst. 43(2), 1\u201347 (2025)","journal-title":"ACM Trans. Inform. Syst."},{"key":"1397_CR36","doi-asserted-by":"crossref","unstructured":"Hou, Y., He, Z., McAuley, J., Zhao, W.X.: Learning vector-quantized item representation for transferable sequential recommenders. In: Proceedings of the ACM Web Conference 2023, pp. 1162\u20131171 (2023)","DOI":"10.1145\/3543507.3583434"},{"key":"1397_CR37","doi-asserted-by":"crossref","unstructured":"Hou, Y., Mu, S., Zhao, W.X., Li, Y., Ding, B., Wen, J.-R.: Towards universal sequence representation learning for recommender systems. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 585\u2013593 (2022)","DOI":"10.1145\/3534678.3539381"},{"key":"1397_CR38","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Yuan, F., Song, Y., Li, Y., Fu, J., Yang, F., Pan, Y., Ni, Y.: Where to go next for recommender systems? id-vs. modality-based recommender models revisited. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2639\u20132649 (2023)","DOI":"10.1145\/3539618.3591932"},{"key":"1397_CR39","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, M., Li, J., Fu, J., Shen, X., Shang, J., McAuley, J.: Text is all you need: Learning language representations for sequential recommendation. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1258\u20131267 (2023)","DOI":"10.1145\/3580305.3599519"},{"key":"1397_CR40","doi-asserted-by":"crossref","unstructured":"Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In: Proceedings of the 16th ACM Conference on Recommender Systems, pp. 299\u2013315 (2022)","DOI":"10.1145\/3523227.3546767"},{"key":"1397_CR41","doi-asserted-by":"crossref","unstructured":"Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., He, X.: Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 1007\u20131014 (2023)","DOI":"10.1145\/3604915.3608857"},{"key":"1397_CR42","unstructured":"Cui, Z., Ma, J., Zhou, C., Zhou, J., Yang, H.: M6-rec: Generative pretrained language models are open-ended recommender systems, (2022). arXiv preprint arXiv:2205.08084"},{"key":"1397_CR43","doi-asserted-by":"crossref","unstructured":"Dai, S., Shao, N., Zhao, H., Yu, W., Si, Z., Xu, C., Sun, Z., Zhang, X., Xu, J.: Uncovering chatgpt\u2019s capabilities in recommender systems. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 1126\u20131132 (2023)","DOI":"10.1145\/3604915.3610646"},{"key":"1397_CR44","doi-asserted-by":"crossref","unstructured":"Zhai, J., Zheng, X., Wang, C.-D., Li, H., Tian, Y.: Knowledge prompt-tuning for sequential recommendation. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 6451\u20136461 (2023)","DOI":"10.1145\/3581783.3612252"},{"key":"1397_CR45","unstructured":"Liu, J., Liu, C., Zhou, P., Lv, R., Zhou, K., Zhang, Y.: Is chatgpt a good recommender? a preliminary study, (2023). arXiv preprint arXiv:2304.10149"},{"key":"1397_CR46","doi-asserted-by":"crossref","unstructured":"Liao, J., Li, S., Yang, Z., Wu, J., Yuan, Y., Wang, X., He, X.: Llara: Large language-recommendation assistant. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1785\u20131795 (2024)","DOI":"10.1145\/3626772.3657690"},{"key":"1397_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Feng, F., Zhang, J., Bao, K., Wang, Q., He, X.: Collm: Integrating collaborative embeddings into large language models for recommendation. IEEE Trans. Knowl. Data Eng. (2025)","DOI":"10.1109\/TKDE.2025.3540912"},{"key":"1397_CR48","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824\u201324837 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1397_CR49","first-page":"11809","volume":"36","author":"S Yao","year":"2023","unstructured":"Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., Narasimhan, K.: Tree of thoughts: Deliberate problem solving with large language models. Adv. Neural. Inf. Process. Syst. 36, 11809\u201311822 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1397_CR50","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wang, Z., Su, Y., Tong, H., Song, Y.: Rethinking the bounds of llm reasoning: Are multi-agent discussions the key? In: 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024, pp. 6106\u20136131 (2024). Association for Computational Linguistics (ACL)","DOI":"10.18653\/v1\/2024.acl-long.331"},{"key":"1397_CR51","doi-asserted-by":"crossref","unstructured":"Chen, J., Saha, S., Bansal, M.: Reconcile: Round-table conference improves reasoning via consensus among diverse llms. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7066\u20137085 (2024)","DOI":"10.18653\/v1\/2024.acl-long.381"},{"key":"1397_CR52","doi-asserted-by":"crossref","unstructured":"Yan, H., Zhu, Q., Wang, X., Gui, L., He, Y.: Mirror: Multiple-perspective self-reflection method for knowledge-rich reasoning. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7086\u20137103 (2024)","DOI":"10.18653\/v1\/2024.acl-long.382"},{"key":"1397_CR53","doi-asserted-by":"crossref","unstructured":"Li, W., Wei, W., Qu, X., Mao, X.-L., Yuan, Y., Xie, W., Chen, D.: Trea: Tree-structure reasoning schema for conversational recommendation. CoRR. (2023)","DOI":"10.18653\/v1\/2023.acl-long.167"},{"key":"1397_CR54","unstructured":"Creswell, A., Shanahan, M.: Faithful reasoning using large language models, (2022). arXiv preprint arXiv:2208.14271"},{"key":"1397_CR55","doi-asserted-by":"crossref","unstructured":"Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Le\u00a0Bras, R., Choi, Y.: Maieutic prompting: Logically consistent reasoning with recursive explanations. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 1266\u20131279 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.82"},{"key":"1397_CR56","unstructured":"Wang, X., Wei, J., Schuurmans, D., Le, Q.V., Chi, E.H., Narang, S., Chowdhery, A., Zhou, D.: Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"1397_CR57","unstructured":"Bi, Z., Han, K., Liu, C., Tang, Y., Wang, Y.: Forest-of-thought: Scaling test-time compute for enhancing llm reasoning, (2024). arXiv preprint arXiv:2412.09078"},{"key":"1397_CR58","doi-asserted-by":"crossref","unstructured":"Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., et al.: Graph of thoughts: Solving elaborate problems with large language models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 17682\u201317690 (2024)","DOI":"10.1609\/aaai.v38i16.29720"},{"key":"1397_CR59","first-page":"113519","volume":"37","author":"L Yang","year":"2024","unstructured":"Yang, L., Yu, Z., Zhang, T., Cao, S., Xu, M., Zhang, W., Gonzalez, J.E., Cui, B.: Buffer of thoughts: Thought-augmented reasoning with large language models. Adv. Neural. Inf. Process. Syst. 37, 113519\u2013113544 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1397_CR60","first-page":"64735","volume":"37","author":"D Zhang","year":"2024","unstructured":"Zhang, D., Zhoubian, S., Hu, Z., Yue, Y., Dong, Y., Tang, J.: Rest-mcts*: Llm self-training via process reward guided tree search. Adv. Neural. Inf. Process. Syst. 37, 64735\u201364772 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1397_CR61","doi-asserted-by":"crossref","unstructured":"Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., Xie, X.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726\u2013735 (2021)","DOI":"10.1145\/3404835.3462862"},{"key":"1397_CR62","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, J., Dang, Y., Liang, Y., Liu, Q., Guo, G., Zhao, J., Wang, X.: Cora: Collaborative information perception by large language model\u2019s weights for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 12246\u201312254 (2025)","DOI":"10.1609\/aaai.v39i12.33334"},{"key":"1397_CR63","unstructured":"Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al.: The llama 3 herd of models, (2024). arXiv preprint arXiv:2407.21783"},{"key":"1397_CR64","unstructured":"Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., Casas, D.d.l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al.: Mistral 7b, (2023). arXiv preprint arXiv:2310.06825"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01397-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-025-01397-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01397-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:21:19Z","timestamp":1770290479000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-025-01397-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,9]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1397"],"URL":"https:\/\/doi.org\/10.1007\/s11280-025-01397-1","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,9]]},"assertion":[{"value":"13 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}],"article-number":"11"}}