{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T06:27:55Z","timestamp":1759991275274,"version":"3.41.0"},"reference-count":65,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","award":["EP\/R018634\/1"],"award-info":[{"award-number":["EP\/R018634\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>\n            Multi-modal interactive recommender systems (MMIRS) can effectively guide users towards their desired items through multi-turn interactions by leveraging the users\u2019 real-time feedback (in the form of natural-language critiques) on previously recommended items (such as images of fashion products). In this scenario, the users\u2019 preferences can be expressed by both the users\u2019 past interests from their historical interactions and their current needs from the real-time interactions. However, it is typically challenging to make satisfactory personalised recommendations across multi-turn interactions due to the difficulty in balancing the users\u2019 past interests and the current needs for generating the users\u2019 state (i.e., current preferences) representations over time. However, hierarchical reinforcement learning has been successfully applied in various fields by decomposing a complex task into a hierarchy of more easily addressed subtasks. In this journal article, we propose a novel personalised multi-modal interactive recommendation model (PMMIR) using hierarchical reinforcement learning to more effectively incorporate the users\u2019 preferences from both their past and real-time interactions. In particular, PMMIR decomposes the personalised interactive recommendation process into a sequence of two subtasks with hierarchical state representations: a first subtask where a history encoder learns the users\u2019 past interests with the hidden states of history for providing personalised initial recommendations and a second subtask where a state tracker estimates the current needs with the real-time estimated states for updating the subsequent recommendations. The history encoder and the state tracker are jointly optimised with a single objective by maximising the users\u2019 future satisfaction with the recommendations. Following previous work, we train and evaluate our PMMIR model using a user simulator that can generate natural-language critiques about the recommendations as a surrogate for real human users. Experiments conducted on two derived fashion datasets from two well-known public datasets demonstrate that our proposed PMMIR model yields significant improvements in comparison to the existing state-of-the-art baseline models. The datasets and code are publicly available at:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/yashonwu\/pmmir\">https:\/\/github.com\/yashonwu\/pmmir<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1145\/3651169","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T12:25:41Z","timestamp":1709555141000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Personalised Multi-modal Interactive Recommendation with Hierarchical State Representations"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-0122","authenticated-orcid":false,"given":"Yaxiong","family":"Wu","sequence":"first","affiliation":[{"name":"University of Glasgow, Glasgow, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3143-279X","authenticated-orcid":false,"given":"Craig","family":"Macdonald","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4701-3223","authenticated-orcid":false,"given":"Iadh","family":"Ounis","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543846"},{"key":"e_1_3_2_3_2","first-page":"4959","volume-title":"CVPR","author":"Baldrati Alberto","year":"2022","unstructured":"Alberto Baldrati, Marco Bertini, Tiberio Uricchio, and Alberto Del Bimbo. 2022. 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