Improving Recommendation of Similar Games by Incorporating Gameplay Video Features

Mobile gaming has become one of the fast growing industries due to the rise of the mobile market. The number of mobile games has increased to over 200 thousands on App Store, not to mention there are still plenty of games on other platforms. However, as mentioned by Yang et al. in their work, current systems fail to provide sufficient support for users to find specific games that meet their needs, especially for similar games. The problem is also supported by our qualitative study and evaluation result. According to our similarity rankings collected from 286 people, current recommender system for similar games produces over 80% dissimilar games on average. We present a novel idea that by considering users definition of similarity between games, we can significantly improve recommendations for similar games by 42%. We demonstrate our idea by implementing a recommender system that incorporates motion features of gameplay video, which is one important similarity factor mentioned by our participants in the qualitative study. Our results show that by considering only gameplay motion features, our system can generate recommendations that outperform existing recommendations for similar games.