Intelligent Application of Digital Information Technology Based on Top-N Recommendation Method

Author:

Hu Liyan

Publisher

Springer Nature Singapore

Reference12 articles.

1. Liu, F.K., Lee, H.J.: Use of social network information to enhance collaborative filtering performance. Expert Syst. Appl. 37(7), 4772–4778 (2009)

2. Liang, L.J., Li, Y.G., Zhang, N.N., Zhang, X., Wang, D.: Collaborative filtering algorithm integrating user features to optimize clustering. J. Intell. Syst. 15(6), 1091–1096 (2020). (in Chinese)

3. Najafabadi, M.K., Mahrin, M.N., Chuprat, S., Sarkan, H.D.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on iimplicit data. Comput. Hum. Behav. 67(2), 113–128 (2017)

4. Zhou, Y.T., Zhang, X.Y., Li, F.l., Liu, S.C., Jiao, J.l., & Tian, D.: SVDPP recommendation algorithm optimized by q-learning algorithm. Comput. Eng. 47(2), 46–51 (2021). (in Chinese)

5. El, A.W.H., Ali, A.F., Slowik, A.: Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system. Neural Comput. Appl. 33(12), 7145–7164 (2020)

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