The massive number of mobile games available necessitates a technique to help the consumer find the right game at the right time. This paper introduces HybridRank, a novel hybrid algorithm to deliver recommendations for mobile games. This technique is based on a personalized random walk approach, with the incorporation of both content-based and user-based information in the formulation of the recommendations. This technique is evaluated against traditional neighborhood based collaborative filtering and content-based recommendation algorithms. This paper also explores the fact that this algorithm can also be used to help alleviate the cold start problem that is associated with little user data.