The traditional collaborative filtering recommendation algorithm has the defects of sparse score matrix, weak scalability and user interest deviation, which lead to the low efficiency of algorithm and low accuracy of score prediction. Aiming at the above problems, this paper proposed a time-weighted collaborative filtering algorithm based on improved Mini Batch K-Means clustering. Firstly, the algorithm selected the Pearson correlation coefficient to improve the Mini Batch K-Means clustering, and used the improved Mini Batch K-Means algorithm to cluster the sparse scoring matrix, calculated the user interest score to complete the filling of the sparse matrix. Then, considering the influence of user interest drift with time, the algorithm introduced the Newton cooling time-weighted to improve user similarity. And then calculated user similarity based on the filled score matrix, which helped to get the last predicted score of unrated items The experimental results show that, compared with the traditional collaborative filtering algorithms, the mean absolute error of Proposed improved algorithm is d, and the Precision, Recall and F1 value of MBKT-CF also get a large improvement, which has a higher rating prediction accuracy.
Focus on the problem that the traditional loop closure detection algorithm is unstable and easy to fail in dynamic scene, an algorithm that can accurately detect the closed loop under the dynamic scene is proposed. First of all, the algorithm for distinguishing dynamic and static features based on scene flow is improved. Then, dynamic feature points are removed and clustering is performed. The TF-IDF entropy of each node of the image in the visual dictionary tree is used as the weight of the image in the visual words, and a score vector is constructed to describe the scene. Finally, the negative exponent power function is used to calculate the similarity of the two images.. After the final closed-loop confirmation, the final key frame with the current frame closed-loop is obtained. The experiment of the actual scene shows that the algorithm proposed in this paper can effectively detect the loop closure in the dynamic scene.
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