Collaborative filtering (CF) has successfully achieved application in personalized recommendation systems. The singular value decomposition (SVD)++ algorithm is employed as an optimized SVD algorithm to enhance the accuracy of prediction by generating implicit feedback. However, the SVD++ algorithm is limited primarily by its low efficiency of calculation in the recommendation. To address this limitation of the algorithm, this study proposes a novel method to accelerate the computation of the

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... D++ algorithm, which can help achieve more accurate recommendation results. The core of the proposed method is to conduct a backtracking line search in the SVD++ algorithm, optimize the recommendation algorithm, and find the optimal solution via the backtracking line search on the local gradient of the objective function. The algorithm is compared with the conventional CF algorithm in the FilmTrust, MovieLens 1 M and 10 M public datasets. The effectiveness of the proposed method is demonstrated by comparing the root mean square error, absolute mean error and recall rate simulation results.