Effective　user　interest　prediction　is　significant　for　service　providers　in　a　set　of　application　scenarios　such　as　user　behavior　analysis　and　resource　recommendation.　However,　existing　approaches　are　either　incomplete　or　proprietary.　In　this　paper,　user　interest　prediction　based　on　the　Markov　chain　modeling　on　clustered　users　is　proposed　with　the　following　procedure:　collect　dataset　from　4613　users　and　more　than　16　million　messages　from　Sina　Weibo;　obtain　each　user＇s　interest　eigenvalue　sequence　and　establish　single-Markov　chain　model;　and　implement　user　clustering　algorithm　for　the　multi-Markov　chain　construction　in　order　to　divide　users　into　a　set　of　predefined　interest　categories.　The　proposed　solution　is　capable　of　predicting　both　long-term　and　short-term　user　interests　based　on　a　suitable　selection　of　the　initial　state　distribution,　lambda.　The　proposed　solution　also　proves　that　short-term　interests　are　consistent　with　long-term　interests　if　the　influences　of　social　or　user-related　events　that　cause　interruptions　(　e.g.,　earthquake　and　birthday)　are　not　considered.　Furthermore,　experiments　show　that　the　proposed　solution　is　feasible　and　efficient　and　can　achieve　a　higher　accuracy　of　prediction　than　that　of　the　other　approaches　such　as　Support　Vector　Machine　(　SVM)　and　K-means.　Copyright　(C)　2015　John　Wiley　&　Sons,　Ltd.