1 |
Z. Batmaz, A. Yurekli, A. Bilge, and C. Kaleli, "A Review on Deep Learning for Recommender Systems: Challenges and Remedies," Artificial Intelligence Review, Vol. 52, No. 1, pp. 1-37, 2019. DOI: 10.1007/s10462-018-9654-y
DOI
|
2 |
M. Aamir and M. Bhusry, "Recommendation System: State of the Art Approach," International Journal Computer Applications, Vol. 120, No. 12, pp. 25-32, 2015. DOI: 10.5120/21281-4200
DOI
|
3 |
M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, "Evaluating Collaborative Filtering Recommender Algorithms: A Survey," IEEE Access, Vol. 6, pp. 74003-74024, 2018. DOI: 10.1109/ACCESS.2018.2883742
DOI
|
4 |
B. Shao, X. Li, and G. Bian, "A Survey of Research Hotspots and Frontier Trends of Recommendation Systems from the Perspective of Knowledge Graph," Expert Systems with Applications, Vol. 165, 2021. DOI: 10.1016/j.eswa.2020.113764
DOI
|
5 |
J. Bobadilla, F. Serradilla, and J. Bernal, "A New Ccollaborative Filtering Metric that Improves the Behavior of Recommender Systems," Knowledge-Based Systems, Vol. 23, No. 6, pp. 520-528, 2010. DOI: 10.1016/j.knosys.2010.03.009
DOI
|
6 |
R. A. Mancisidor, M. Kampffmeyer, K. Aas, and R. Jenssen, "Learning Latent Representations of Bank Customers with the Variational Autoencoder," Expert Systems with Applications, Vol. 164, 2021. DOI: 10.1016/j.eswa.2020.114020
DOI
|
7 |
K. G. Saranya, G. S. Sadasivam, and M. Chandralekha, "Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique," Indian Journal of Science and Technology, Vol. 9, No. 29, Aug. 2016. DOI: 10.17485/ijst/2016/v9i29/91060
DOI
|
8 |
S. Kosub, "A Note on the Triangle Inequality for the Jaccard Distance," Pattern Recognition Letters, Vol. 120, pp. 36-38, 2019. DOI: 10.1016/j.patrec.2018.12.007
DOI
|
9 |
A. Iftikhar, M. A. Ghazanfar, M. Ayub, Z. Mehmood, and M. Maqsood, "An Improved Product Recommendation Method for Collaborative Filtering," IEEE Access, Vol. 8, pp. 123841-123857, 2020. DOI: 10.1109/ACCESS.2020.3005953
DOI
|
10 |
S. Bag, S. K. Kumar, and M. K. Tiwari, "An Efficient Recommendation Generation using Relevant Jaccard Similarity," Information Sciences, Vol. 483, pp. 53-64, 2019. DOI: 10.1016/j.ins.2019.01.023
DOI
|
11 |
S. Lee, "Improving Jaccard Index using Genetic Algorithms for Collaborative Filtering," Lecture Notes on Computer Science, 10385, pp. 378-385, 2017. DOI: 10.1007/978-3-319-61824-1_41
|
12 |
S. Lee, "Performance Analysis of Similarity Reflecting Jaccard Index for Solving Data Sparsity in Collaborative Filtering," The Journal of Korean Association of Computer Education, Vol. 19, No. 4, pp. 59-66, July 2016.
DOI
|
13 |
J. Guo, J. Deng, X. Ran, Y. Wang, and H. Jin, "An Efficient and Accurate Recommendation Strategy using Degree Classification Criteria for Item-based Collaborative Filtering," Expert Systems with Applications, Vol. 164, 2021. DOI: 10.1016/j.eswa.2020.113756
DOI
|
14 |
Y. Mu, N. Xiao, R. Tang, L. Luo, and X. Yin, "An Efficient Similarity Measure for Collaborative Filtering," Procedia Computer Science, Vol. 147, pp. 416-421, 2019. DOI: 10.1016/j.procs.2019.01.258
DOI
|
15 |
B. Zhu, R. Hurtado, J. Bobadilla, and F. Ortega, "An Efficient Recommender System Method Based on the Numerical Relevances and the Non-Numerical Structures of the Ratings," IEEE Access, Vol. 6, pp. 49935-49954, 2018. DOI: 10.1109/ACCESS.2018.2868464
DOI
|
16 |
S.-B. Sun, Z.-H. Zhang, X.-L. Dong, H.-R. Zhang, T.-J. Li, L. Zhang, and F. Min, "Integrating Triangle and Jaccard Similarities for Recommendation," PLoS ONE, Vol. 12, No. 8, 2017. DOI: 10.1371/journal.pone.0183570
DOI
|