The　theory　of　hesitant　fuzzy　set　is　powerful　in　capturing　fuzziness　when　a　decision　maker　hesitates　among　some　possible　values　in　assessment.　In　this　paper,　a　novel　method　is　developed　for　weight　determination　based　on　the　hesitant　fuzzy　cosine　similarity.　This　method　fully　considers　the　impact　of　each　criterion　as　well　as　their　mutual　relationship　on　decision　analysis.　Additionally,　we　extend　this　cosine　similarity-based　method　for　deriving　priorities　from　a　hesitant　multiplicative　preference　relation.　This　method　has　a　clearer　modeling　mechanism　that　the　priority　vector　derived　from　a　hesitant　multiplicative　preference　relation　should　be　as　highly　cosine-similarity　correlated　with　its　column　vectors　as　possible.　Then　an　optimization　model　can　be　sequentially　established.　After　that,　the　proposed　methods　are　demonstrated　with　two　numerical　examples　and　compared　with　other　similar　approaches　to　show　the　validity　and　superiority.