Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

Since years, online social networks have evolved from profile and communication websites to online portals where people interact with each other, share and consume multimedia-enriched data and play different types of games. Due to the immense popularity of these online games and their huge revenue potential, the number of these games increases every day, resulting in a current offering of thousands of online social games. In this paper, the applicability of neighborhood-based collaborative filtering (CF) algorithms for the recommendation of online social games is evaluated. This evaluation is based on a large dataset of an online social gaming platform containing game ratings (explicit data) and online gaming behavior (implicit data) of millions of active users. Several similarity metrics were implemented and evaluated on the explicit data, implicit data and a combination thereof. It is shown that the neighborhood-based CF algorithms greatly outperform the content-based algorithm, currently often used on online social gaming websites. The results also show that a combined approach, i.e., taking into account both implicit and explicit data at the same time, yields overall good results on all evaluation metrics for all scenarios, while only slightly performing worse compared to the strengths of the explicit or implicit only approaches. The best performing algorithms have been implemented in a live setup of the online game platform.