Sequential recommendations are the personalized, dynamic recommendations are achieved by modeling the sequential behavior of users. However, in the real world, user behavior data often exhibits high sparsity, while the transition relation between items in the behavior sequence changes with item characteristics. Therefore, how to capture the collaborative relation between users and items, while also capturing the transition patterns between items, becomes a crucial problem in sequential recommendation. To address this problem, this paper proposes a kind of collective matrix factorization method that fuses transition relation regularization. The method jointly decomposes the user-item interaction matrix and the Markov transition matrix between items. It sets shared item representation factors during the decomposition process to capture both collaborative relationships and transfer relationships. This alleviates the sparsity problem in user behavior data, thereby achieving effective sequence recommendation. Experimental comparison and analysis on five real-world datasets containing POIs, e-commerce user behavior sequences, movie and music ratings demonstrate that the proposed method outperforms existing state-of-the-art algorithms for sequence recommendation.