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Existing bundle generation methods mainly utilized user\u2019s preference from historical interactions in common recommendation paradigm, and ignored the potential textual query which is user\u2019s current explicit intention. There can be a scenario in which a user proactively queries a bundle with some natural language description, the system should be able to generate a bundle that exactly matches the user\u2019s intention through the user\u2019s query and preferences. In this work, we define this user-friendly scenario as\n                    <jats:bold>Query-based Bundle Generation<\/jats:bold>\n                    task and propose a novel framework\n                    <jats:bold>Text2Bundle<\/jats:bold>\n                    that leverages both the user\u2019s short-term interests from the query and the user\u2019s long-term preferences from the historical interactions. Our framework consists of three modules: (1) an intention extractor based on Large Language Model that mines the user\u2019s fine-grained interests from the query; (2) a unified state encoder that learns the current bundle context state and the user\u2019s preferences based on historical interaction and current query; and (3) a bundle generator that generates personalized and complementary bundles using reinforcement learning with specifically designed rewards. We conduct extensive experiments on three real-world datasets and demonstrate the effectiveness of our framework compared with several state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1145\/3706125","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:49:07Z","timestamp":1743140947000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Text2Bundle: Towards Personalized Query-based Bundle Generation"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9883-2154","authenticated-orcid":false,"given":"Juntong","family":"Hu","sequence":"first","affiliation":[{"name":"Tongji University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2217-6215","authenticated-orcid":false,"given":"Shixuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tongji University","place":["Shanghai, China"]},{"name":"iFLYTEK Research","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3659-7289","authenticated-orcid":false,"given":"Chuan","family":"Cui","sequence":"additional","affiliation":[{"name":"Tongji University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1919-6749","authenticated-orcid":false,"given":"Qi","family":"Shen","sequence":"additional","affiliation":[{"name":"Tongji University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8728-8366","authenticated-orcid":false,"given":"Yu","family":"Ji","sequence":"additional","affiliation":[{"name":"Tongji University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5937-3907","authenticated-orcid":false,"given":"Zhihua","family":"Wei","sequence":"additional","affiliation":[{"name":"Tongji University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/SNAMS58071.2022.10062688"},{"key":"e_1_3_2_3_2","first-page":"60","volume-title":"WWW (WWW\u201919)","author":"Bai Jinze","year":"2019","unstructured":"Jinze Bai, Chang Zhou, Junshuai Song, Xiaoru Qu, Weiting An, Zhao Li, and Jun Gao. 2019. 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