HybridRank: A Hybrid Content-Based Approach To Mobile Game Recommendations.

Abstract

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 hy-brid algorithm to deliver recommendations for mobile games. This technique is based on a personalised random walk ap-proach, with the incorporation of both content-based and user-based information in the formulation of the recommen-dations. This technique is evaluated against traditional neigh-bourhood based collaborative filtering and content-based rec-ommendation 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.[1] Online evaluations were conducted and results yield that the approach presented performed the best in both a controlled testing environment as well as in live production. This algo-rithm is currently implemented in a live mobile game plat-form developed by Singapore Telecommunications Ltd called