Cold Start Video Recommendation Using Regression Models Based On Watch Ratio Prediction
DOI:
https://doi.org/10.29284/rhdh8317Keywords:
Cold Start, Regression, Harmonic.Abstract
Video recommendation system cold start problem occurs when there is little or no past interaction data of new users or new video uploaded videos, and it is difficult to predict the user accurately and personalize the video. This paper tackles the cold start case and establishes and compares three predictive models applied to predict a video watch ratio considering constant contextual circumstances; the Linear Regression, Ridge Regression, and XGBoost models. The feature engineering strategies were employed (temporal attributes and one-hot encoding of video duration categories) to have uniform representation across the models. The experimental results demonstrate that Linear Regression and Ridge Regression performed almost perfectly, displaying the highest possible R 2 and Adjusted R 2 values of 1.0000 and the lowest possible error values (MAE, MSE, RMSE), the order of magnitude of the residual values in Linear Regression and Ridge Regression is 10 -1 and 10 -8, respectively. On the contrary, XGBoost achieved more mediocre R that of 0.6338 at reduced error and residual in the range of 10 3. Further evidences of Ridge Regression as a superior predictor model include residual analysis, recommendation ranking behavior, and Taylor Diagram evaluation, which prove that the latter has superior levels of variance, high correlation with the observed data, and predictive stability. Even though the XGBoost embraces nonlinear association, it has more dispersions and variations when the data is set in the mentioned way. In general, the findings demonstrate that Ridge Regression offers the most balanced, accurate and reliable solution to cold start problem in video recommendation system.
We have the following important
References
1. Bobadilla, J.; Ortega, F.; Hernando, A.; Gutiérrez, A. Recommender systems survey. Knowl. Based Syst. 2013, 46, 109–132.
2. Burke, R.; Felfernig, A.; Göker, M. Recommender systems: An overview. AI Mag. 2011, 32, 13–18. [CrossRef]
3. Pazzani, M.J.; Billsus, D., Content-Based Recommendation Systems. In The Adaptive Web: Methods and Strategies of WebPersonalization; Brusilovsky, P., Kobsa, A., Nejdl, W., Eds.; Springer: Berlin/Heidelberg, Germany, 2007; pp. 325–341. [CrossRef]
4. Boutilier, C.; Zemel, R.S.; Marlin, B. Active Collaborative Filtering. In Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence, Acapulco, Mexico, 7–10 August 2003; pp. 98–106.
5. Bennett, J.; Lanning, S. The Netflix Prize. In Proceedings of the KDD Cup and Workshop, San Jose, CA, USA, 12 August 2007.
6. Bobadilla, J.; Ortega, F.; Hernando, A.; Bernal, J. A collaborative filtering approach to mitigate the new user cold start problem. Knowl. Based Syst. 2012, 26, 225–238. [CrossRef]
7. Sahu, A.; Dwivedia, P.; Kant, V. Tags and Item Features as a Bridge for Cross-Domain Recommender Systems. Procedia Comput. Sci. 2018, 125, 624–631. [CrossRef]
8. Wei, J.; He, J.; Chen, k.; Zhou, Y.; Tang, Z. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 2017, 69, 29–39. [CrossRef]
9. Gonzalez Camacho, L.; Nice Alves-Souza, S. Social network data to alleviate cold-start in recommender system: A systematic review. Inf. Process. Manag. 2018, 54, 529–544. [CrossRef]
10. Natarajan, S.; Vairavasundaram, S.; Natarajan, S.; Gandomi, A. Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data. Expert Syst. Appl. 2020, 149, 113248. [CrossRef]
11. Hoang-Son, L. Dealing with the new user cold-start problem in recommender systems: A comparative review. Inf. Syst. 2016, 58, 87–104.
12. Viktoratos, I.; Tsadiras, A.; Bassiliades, N. Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems. Expert Syst. Appl. 2018, 101, 78–90. [CrossRef]
13. Hernando, A.; Bobadilla, J.; Ortega, F.; Gutiérrez, A. A probabilistic model for recommending to new cold-start non-registered users. Inf. Sci. 2017, 376, 216–232. [CrossRef]
14. Lika, B.; Kolomvatsos, K.; Hadjiefthymiades, S. Facing the cold start problem in recommender systems. Expert Syst. Appl. 2014, 41, 2065–2073. [CrossRef]
15. Chien, C.; Yu-Hao,W.; Meng-Chieh, C.; Yu-Chun, S. An effective recommendation method for cold start new users using trust and distrust networks. Inf. Sci. 2013, 224, 19–36. [CrossRef]
16. Herce-Zelaya, J.; Porcel, C.; Bernabé-Moreno, J.; Tejeda-Lorente, A.; Herrera-Viedma, E. New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. Inf. Sci. 2020, 536, 156–170 [CrossRef]
17. Zhang, Y.; Shi, Z.; Zuo, W.; Yue, L.; Li, X. oint Personalized Markov Chains with social network embedding for cold-start recommendation. Neurocomputing 2019, 386 , 208–220. [CrossRef]
18. García-Sánchez, F.; Colomo-Palacios, R.; Valencia-García, R. A social-semantic recommender system for advertisements. Inf. Process. Manag. 2020, 57, 102153. [CrossRef]
19. Esmaeili, L.; Mardani, S.; Golpayegani, S.; Madar, Z. A novel tourism recommender system in the context of social commerce. Expert Syst. Appl. 2020, 149, 113301. [CrossRef]
20. Panda, D.K.; Ray, S. Approaches and algorithms to mitigate cold start problems in recommender systems: A systematic literature review. J. Intell. Inf. Syst. 2022, 59, 341–366. [CrossRef]
21. Ramezani, M.; Akhlaghian Tab, F.; Abdollahpouri, A.; Abdulla Mohammad, M. A new generalized collaborative filtering approach on sparse data by extracting high confidence relations between users. Inf. Sci. 2021, 570, 323–341. [CrossRef]
22. Viktoratos, I.; Tsadiras, A. Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework. Information 2021, 12, 480. [CrossRef]
23. Majumdar, A.; Jain, A. Cold-start, warm-start and everything in between: An autoencoder based approach to recommendation. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017; pp. 3656–3663. [CrossRef]
24. Feng, J.; Xia, Z.; Feng, X.; Peng, J. RBPR: A hybrid model for the new user cold start problem in recommender systems. Knowl. Based Syst. 2021, 214, 106732. [CrossRef]
25. Vagliano, I.; Galke, L. ecommendations for item set completion: On the semantics of item co-occurrence with data sparsity, input size, and input modalities. Inf Retr. 2022, 25, 269–305. [CrossRef]
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
