Online Video Game Recommendation System Using Content And Collaborative Filtering Techniques
C Bharathipriya, Akash Sreenivasu, B. T. Sampath Kumar · 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) · 2021
The virtual amusement quarter is one in all the quickest developing in current years. In the case of video games, the productions of several maximum famous titles are with movie productions. The video game sale is within side of millions, and but there are very few works on the advice of videogames. Video games and online game gamers generate huge quantities of data, as the entirety they do inside a sport is recorded. Its diversification has dramatically extended the quantity of customers engaged in online groups of this amusement area, and consequently, the quantity and sorts of video games available. The statistics context overload underpins the improvement of recommender structures that would leverage the statistics that the online game structures collect, for this reason following the fashion of recent video games coming out each year. Here, a new method of online game recommendation is provided, via the usage of collaborative filtering. In order to enhance the recommendations, a new approach for estimating implicit rankings is anticipated and takes the hours of play and recommends better. The proposed recommender device improves the effects of different strategies provided withinside the kingdom of the art.