Abstract
The ratings of TV programs are irregular, and most of the viewers do not score every program they have watched, which leads to uneven distribution of user-program ratings matrix and low sparseness of the matrix in the recommendation system. For the recommendation algorithm, the sparsity of the input matrix has a great impact on the accuracy of the recommendation algorithm. Especially in the face of big data sets, the problems are enlarged. Aiming at these problems, a program recommendation algorithm based on LDA topic model and improved ALS collaborative filtering is proposed. This algorithm combines the program similarity matrix obtained from program features, preprocesses the score matrix to get the prescore matrix as input, and then dynamically weights the user and program features to improve the collaborative filtering algorithm to achieve the effect of “stable recommendation” and “multiple recommendation.” The simulation results show that the parallel operation of Spark MLlib algorithm library not only solves the problem of low timeliness of big data sets but also stabilizes the average RMES of hybrid recommendation algorithm at about 0.52. Compared with the traditional ALS collaborative filtering recommendation algorithm, the effect is significantly improved.
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Peng, A., Liu, H. (2021). Hybrid Program Recommendation Algorithm Based on Spark MLlib in Big Data Environment. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_48
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DOI: https://doi.org/10.1007/978-981-15-3753-0_48
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