Fully content-based IMDb movie recommendation engine with Pearson similarity

Chutian Wei; Xinyu Chen; Zhenning Tang; Wen Cheng

doi:10.1117/12.2624930

6 December 2021 Fully content-based IMDb movie recommendation engine with Pearson similarity

Chutian Wei, Xinyu Chen, Zhenning Tang, Wen Cheng

Proceedings Volume 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021); 120850M (2021) https://doi.org/10.1117/12.2624930
Event: 2021 International Conference on Green Communication, Network, and Internet of Things, 2021, Kunming, China

Abstract

With the advancement of technology and the updating of information, more people choose to watch movies on the Internet. In the world of brust data, users often encounter the difficulty of finding their favorite movies. Implementing the movie recommendation system of the media platform is one of the most effective ways to solve this problem. In order to avoid the imbalance of user data in the actual operation process, the content-based recommendation model is adopted in this research, aiming to find their similar variables in each movie, to calculate similarity index between each movie through the matrix and Pearson formula. The advantage of this method is that the potential interest of users for each movie can be discovered, and the probability of the movie being recommended will not be caused by the problem of the popularity of the movie.

Citation Download Citation

Chutian Wei, Xinyu Chen, Zhenning Tang, and Wen Cheng "Fully content-based IMDb movie recommendation engine with Pearson similarity", Proc. SPIE 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021), 120850M (6 December 2021); https://doi.org/10.1117/12.2624930

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