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Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user\u2019s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user\u2019s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user\u2019s preferences. Compared with other methods, our method has better accuracy on recommendation.<\/jats:p>","DOI":"10.1186\/s13638-019-1619-6","type":"journal-article","created":{"date-parts":[[2019,12,21]],"date-time":"2019-12-21T12:02:29Z","timestamp":1576929749000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Personalized project recommendations: using reinforcement learning"],"prefix":"10.1186","volume":"2019","author":[{"given":"Faxin","family":"Qi","sequence":"first","affiliation":[]},{"given":"Xiangrong","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yingjie","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,21]]},"reference":[{"key":"1619_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.jnca.2015.04.007","volume":"55","author":"YJ Wang","year":"2015","unstructured":"Y.J. 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