Abstract
The basic recommendation algorithms include item-based CF algorithms and recommendation algorithms based on machine learning. The following article focuses on the system theory and implementation of these two basic algorithms. Based on their core ideas, a new method is proposed, which is a hybrid recommendation algorithm based on the CF algorithm and LFM algorithm. The algorithm uses a latent factor model to generate user preference matrix and item hidden feature matrix, and learns to update the matrix through gradient descent, and then calculates the similarity between items according to the calculation method in collaborative filtering, and finally, after weighting, obtains Top-N recommendation results. The algorithm has been verified, which improves the accuracy of recommendation, improves the shortcomings of collaborative filtering algorithm that only analyzes and displays features, and also alleviates the influence of sparse matrix on recommendation results.
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Notes
- 1.
1This work is supported by the National Key Research and Development Program of China (No. 2019YFB1405500).
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Xie, W., Sun, X. (2021). A Hybrid Recommendation Algorithm Based on Latent Factor Model and Collaborative Filtering. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds) Intelligent Equipment, Robots, and Vehicles. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1469. Springer, Singapore. https://doi.org/10.1007/978-981-16-7213-2_9
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DOI: https://doi.org/10.1007/978-981-16-7213-2_9
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