Probabilistic Models for Recommender Systems

• Thesis title in Czech: Pravděpodobnostní modely pro doporučovací systémy
• Thesis title in English: Probabilistic Models for Recommender Systems
• Key words: strojové učení|pravděpodobnostní modely|doporučovací systémy
• English key words: Machine learning|Probabilistic models|Recommender system|Topic models
• Academic year of topic announcement: 2020/2021
• Thesis type: diploma thesis
• Thesis language: angličtina
• Department: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
• Supervisor: Mgr. Marta Vomlelová, Ph.D.
• Author: hidden - assigned and confirmed by the Study Dept.
• Date of registration: 13.10.2020
• Date of assignment: 24.11.2020
• Confirmed by Study dept. on: 08.12.2020
• Date and time of defence: 07.09.2022 09:00
• Date of electronic submission: 15.06.2022
• Date of submission of printed version: 24.07.2022
• Date of proceeded defence: 07.09.2022
• Opponents: Mgr. Ladislav Peška, Ph.D.

Guidelines

The student should study the Collaborative Topic Model for Poisson distributed ratings [1] and train a similar model on the extended Movielense dataset. He will consider to improve the training algorithm according the [2] and/or to test the performance of the model on different datasets (for example the Netflix data).

References

[1] Hoa M. Le, Son Ta Cong, Quyen Pham The, Ngo Van Linh, Khoat Than, Collaborative Topic Model for Poisson distributed ratings, International Journal of Approximate Reasoning, Volume 95, 2018, Pages 62-76, ISSN 0888-613X, https://doi.org/10.1016/j.ijar.2018.02.001.

[2] X. Bui, H. Vu, O. Nguyen and K. Than, "MAP Estimation With Bernoulli Randomness, and Its Application to Text Analysis and Recommender Systems," in IEEE Access, vol. 8, pp. 127818-127833, 2020, doi: 10.1109/ACCESS.2020.3008534.