Online game props recommendation with real assessments

Abstract

With the rapid development of smart mobile devices, phone games become an important way of entertainments. Benefitting from sophisticated payment environments of mobile platforms, e.g., Apple APP store, the In-APP purchases which sell equipments or virtual props bring in the main profits for game carriers and developers. Although virtual props from a certain type of smart phone game are monopolized by only one seller, like other commodities, products' recommendation is able to improve the profit margins as well. One main difference between virtual props recommendation and the general good recommendations lies in that the virtual props are closely related to the game contexts, and this will lead to complicated dependencies. Therefore, general recommendation systems without consideration on game contexts cannot perform very well. Besides, multiple types of props in one game may depend on different game characters of players, thus single player trends to buy only appropriate props for improving the skills of his game characters. Moreover, the purchase intensions of players are influenced by multiple factors, and will change over time. Therefore, it is desired recommendation approach to be capable of handling the role dependencies and concept variations. In this paper, we treat the game contexts as events from game log records, and model the game props recommendation into a multi-instance multi-label learning task for utilizing the complicated dependencies and capturing the rank of purchase intentions. We proposed three variants of solutions against the concept variation problem as well. Finally, we conduct comprehensive empirical investigations on real-world data sets and a series of real online smart phone games. The positive experiment results and increasing profit margins validate the remarkable effectiveness of our solutions.

Team Composition in PES2018 Using Submodular Function Optimization

“…In other words, the human-player would be better satisfied if the game could recommend a dream team that will succeed in a new match in PES. This is well aligned with entertainment spirit in the content recommendation in computer games [1]. Hence a The associate editor coordinating the review of this manuscript and approving it for publication was Shuai Han.…”

“…In the recent years, many sport games have appeared and attracted more and more players in game markets. Pro Evolution Soccer 2018 (PES2018) 1 is a popular football game which is produced and released by Konami, 2 it can be played on a personal computer, PS4 or XBOX. This game can be controlled by human or computer players, and can fully simulate a football match.…”

“…Hence a The associate editor coordinating the review of this manuscript and approving it for publication was Shuai Han. 1 https://www.konami.com/wepes/2018/ 2 https://www.konami.com/ team recommender becomes an important feature in a sport game not just limited in PES [2]. In PES, every football player is specified by a set of attributes, e.g.…”