The　sustainability　of　an　open　source　project　is　essential　for　the　long-term　and　reliable　development　of　software.　Most　existing　studies　focus　on　the　recommendation　accuracy　of　bug　report　assignment　while　ignoring　inexperienced　developers　in　the　open　source　community.　This　gives　inexperienced　developers　less　opportunity　to　resolve　bugs　and　can　cause　them　to　gradually　lose　interest　in　the　development　of　open　source　software　(OSS).　To　address　this　problem,　this　article　proposes　a　novel　approach　called　sustainable　recommender　(SusRec)　to　make　sustainable　report　assignments　without　sacrificing　accuracy.　The　SusRec　approach　is　based　on　multimodal　learning　and　ensemble　learning,　and　it　consists　of　two　stages:　the　preprocessing　stage　and　the　developer　scoring　stage.　In　the　preprocessing　stage,　the　approach　selects　candidate　developers　who　have　participated　in　the　resolution　of　bugs　under　the　product　of　a　new　bug　report.　It　then　divides　the　candidate　developers　into　three　types-core　developers,　active　developers,　and　peripheral　developers-according　to　their　experience.　In　the　developer　scoring　stage,　multimodal　learning　is　adopted　to　score　the　three　types　of　bug　report-developer　pairs,　and　ensemble　learning　is　adopted　to　weight　the　scores　of　the　three　types　of　bug　report-developer　pairs　and　recommend　developers　for　bug　reports.　We　conduct　extensive　experiments　using　the　bug　repositories　of　the　Eclipse　and　Mozilla　projects　to　compare　the　proposed　SusRec　approach　with　the　baseline　methods　in　bug　report　assignment.　The　results　demonstrate　that　the　proposed　SusRec　approach　cannot　only　improve　the　accuracy　of　developer　recommendations　for　bug　reports,　but　also　the　sustainability　of　OSS　projects　by　providing　more　opportunities　for　active　developers　and　peripheral　developers　to　participate　in　bug　resolution.