{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:37:59Z","timestamp":1723016279401},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Interactive search, where a set of tags is recommended to users together with search results at each turn, is an effective way to guide users to identify their information need. It is a classical sequential decision problem and the reinforcement learning based agent can be introduced as a solution. The training of the agent can be divided into two stages, i.e., offline and online. Existing reinforcement learning based systems tend to perform the offline training in a supervised way based on historical labeled data while the online training is performed via reinforcement learning algorithms based on interactions with real users. The mis-match between online and offline training leads to a cold-start problem for the online usage of the agent. To address this issue, we propose to employ a simulator to mimic the environment for the offline training of the agent. Users' profiles are considered to build a personalized simulator, besides, model-based approach is used to train the simulator and is able to use the data efficiently. Experimental results based on real-world dataset demonstrate the effectiveness of our agent and personalized simulator.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/710","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5109-5115","source":"Crossref","is-referenced-by-count":2,"title":["Building Personalized Simulator for Interactive Search"],"prefix":"10.24963","author":[{"given":"Qianlong","family":"Liu","sequence":"first","affiliation":[{"name":"Fudan University, China"}]},{"given":"Baoliang","family":"Cui","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"given":"Zhongyu","family":"Wei","sequence":"additional","affiliation":[{"name":"Fudan University, China"}]},{"given":"Baolin","family":"Peng","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}]},{"given":"Haikuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"given":"Hongbo","family":"Deng","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"given":"Jianye","family":"Hao","sequence":"additional","affiliation":[{"name":"Tianjin University, China"}]},{"given":"Xuanjing","family":"Huang","sequence":"additional","affiliation":[{"name":"Fudan University, China"}]},{"given":"Kam-Fai","family":"Wong","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:15Z","timestamp":1564285875000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/710"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/710","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}