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However, conventional recommendation systems that are designed for playing songs matching user preferences cannot be applied for such a situation. This is because previous research showed that listeners\u2019 concentration can be negatively affected not only by music that listeners strongly dislike but also by music that the listeners strongly like. Therefore, when we consider a recommendation system to be used while working, it is desirable to avoid both songs the user likes very much and songs the user dislikes very much. Given this background, we propose <jats:italic>FocusMusicRecommender<\/jats:italic>, a system designed specifically for recommending music to listen to while working. It summarizes songs automatically and plays them successively in order to enable users to give not only \u201cdislike (very much)\u201d feedback via a \u201cskip\u201d button but also \u201clike (very much)\u201d feedback via a \u201ckeep listening\u201d button. The feedback is then combined with the users\u2019 concentration level that is estimated from their behavioral history during the playback of the corresponding song, which allows the system to obtain preference information that distinguishes between \u201clike\u201d and \u201clike very much\u201d without burdening the user who is working. Based on the preference information, the system estimates the preference levels of unplayed songs and prioritizes the songs for subsequent playback by also considering the user\u2019s current concentration level. Our experiments showed the validity and effectiveness of the proposed method, including the accuracy of the concentration level estimation. Moreover, our user study verified the suitability of the recommendation results from both the observed behavior and obtained comments of the participants.\n<\/jats:p>","DOI":"10.1007\/s11257-022-09325-y","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T12:05:37Z","timestamp":1652875537000},"page":"355-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An automated system recommending background music to listen to while working"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2558-735X","authenticated-orcid":false,"given":"Hiromu","family":"Yakura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoyasu","family":"Nakano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masataka","family":"Goto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"key":"9325_CR1","doi-asserted-by":"publisher","unstructured":"Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. 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