{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T05:03:47Z","timestamp":1784351027349,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819233908","type":"print"},{"value":"9789819233915","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3391-5_20","type":"book-chapter","created":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T04:43:49Z","timestamp":1784349829000},"page":"240-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perplexity-Guided Interest Generation with Dual-Feedback Alignment for LLM-based Recommendation"],"prefix":"10.1007","author":[{"given":"Yipu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingkun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1145\/3604915.3608857","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems","author":"K Bao","year":"2023","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. ACM (2023)"},{"key":"20_CR2","unstructured":"Dubey, A., et al.: The Llama 3 Herd of models. arXiv Preprint https:\/\/arxiv.org\/abs\/2407.21783. (2024)"},{"key":"20_CR3","unstructured":"Fu, R., et al.: Sphunc: hyperspherical uncertainty decomposition and causal identification via information geometry. arXiv Preprint https:\/\/arxiv.org\/abs\/2603.01168. (2026)"},{"key":"20_CR4","unstructured":"Fu, R., et al.: Neuro-pareto: calibrated acquisition for costly many-goal search in vast parameter spaces. arXiv Preprint https:\/\/arxiv.org\/abs\/2602.03901. (2026)"},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1145\/3397271.3401063","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"X He","year":"2020","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. ACM (2020)"},{"key":"20_CR6","volume-title":"The Tenth International Conference on Learning Representations","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: The Tenth International Conference on Learning Representations. OpenReview.net (2022)"},{"key":"20_CR7","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv Preprint https:\/\/arxiv.org\/abs\/1611.01144. (2017)"},{"key":"20_CR8","first-page":"197","volume-title":"IEEE International Conference on Data Mining","author":"W Kang","year":"2018","unstructured":"Kang, W., McAuley, J.J.: Self-attentive sequential recommendation. In: IEEE International Conference on Data Mining, pp. 197\u2013206. IEEE Computer Society (2018)"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Kang, Z., et al.: Multimodal multi-agent empowered legal judgment prediction. (2026)","DOI":"10.1109\/ICASSP55912.2026.11462917"},{"key":"20_CR10","first-page":"7602","volume-title":"Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"M Li","year":"2024","unstructured":"Li, M. et al.: From quantity to quality: boosting LLM performance with self-guided data selection for instruction tuning. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 7602\u20137635. Association for Computational Linguistics (2024)"},{"key":"20_CR11","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1145\/3616855.3635845","volume-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","author":"Q Liu","year":"2024","unstructured":"Liu, Q., Chen, N., Sakai, T., Wu, X.: ONCE: boosting content-based recommendation with both open- and closed-source large language models. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 452\u2013461. ACM (2024)"},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"10456","DOI":"10.18653\/v1\/2024.findings-acl.623","volume-title":"Findings of the Association for Computational Linguistics: ACL 2024","author":"D Mekala","year":"2024","unstructured":"Mekala, D., Nguyen, A., Shang, J.: Smaller language models are capable of selecting instruction-tuning training data for larger language models. In: Findings of the Association for Computational Linguistics: ACL 2024, pp. 10456\u201310470. Association for Computational Linguistics, Bangkok, Thailand (2024)"},{"key":"20_CR13","first-page":"188","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing","author":"J Ni","year":"2019","unstructured":"Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 188\u2013197. Association for Computational Linguistics, Hong Kong, China (2019)"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Rajbhandari, S., Rasley, J., Ruwase, O., He, Y.: ZeRO: memory optimizations toward training trillion parameter models. arXiv Preprint https:\/\/arxiv.org\/abs\/1910.02054. (2020)","DOI":"10.1109\/SC41405.2020.00024"},{"key":"20_CR15","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1145\/3626772.3657782","volume-title":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Y Ren","year":"2024","unstructured":"Ren, Y., et al.: Enhancing sequential recommenders with augmented knowledge from aligned large language models. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 345\u2013354. Association for Computing Machinery, New York, NY, USA (2024)"},{"key":"20_CR16","doi-asserted-by":"publisher","first-page":"13176","DOI":"10.18653\/v1\/2024.findings-acl.780","volume-title":"Findings of the Association for Computational Linguistics: ACL 2024","author":"A Tsai","year":"2024","unstructured":"Tsai, A., et al.: Leveraging LLM reasoning enhances personalized recommender systems. In: Findings of the Association for Computational Linguistics: ACL 2024, pp. 13176\u201313188. Association for Computational Linguistics, Bangkok, Thailand (2024)"},{"key":"20_CR17","unstructured":"Zhang, Y., Feng, F., Zhang, J., Bao, K., Wang, Q., He, X.: CoLLM: integrating collaborative embeddings into large language models for recommendation. (2023)"},{"key":"20_CR18","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1145\/3539618.3591755","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., et al.: Reformulating CTR prediction: learning invariant feature interactions for recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1386\u20131395. ACM (2023)"},{"key":"20_CR19","volume-title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. pp. 8384\u20138392. International Joint Conferences on Artificial Intelligence Organization","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Xiong, G., Li, H., Zhao, W.: EDGE: efficient data selection for LLM agents via guideline effectiveness. In: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. pp. 8384\u20138392. International Joint Conferences on Artificial Intelligence Organization (2025)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3391-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T04:43:55Z","timestamp":1784349835000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3391-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"ISBN":["9789819233908","9789819233915"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3391-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"19 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}