Hye-Woo Lee, Noo-ri Kim, and Jee-Hyong Lee. Int. J. Fuzzy Log. Intell. Syst. 2017;17:1-9. https://doi.org/10.5391/IJFIS.2017.17.1.1

An approach for game bot detection in massively multiplayer online role‐playing games (MMORPGs) based on the analysis of game playing behavior is proposed. Since MMORPGs are large‐scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. To cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.

Yeounoh Chung, Noo-ri Kim, Chang-yong Park, and Jee-Hyong Lee. IJFIS 2018;18:29-40. https://doi.org/10.5391/IJFIS.2018.18.1.29

In this paper, a novel television (TV) program recommendation method is proposed by merging multiple preferences. We use channels and genres of programs, which is available information in standalone TVs, as features for the recommendation. The proposed method performs multi-time contextual profiling and constructs multiple-time contextual preference matrices of channels and genres. Since multiple preference models are constructed with different time contexts, there can be conflicts among them. In order to effectively merge the preferences with the minimum number of conflicts, we develop a quadratic programming model. The optimization problem is formulated with a minimum number of constraints so that the optimization process is scalable and fast even in a standalone TV with low computational power. Experiments with a real-world dataset prove that the proposed method is more efficient and accurate than other TV recommendation methods. Our method improves recommendation performance by 5–50% compared to the baselines.

Noo-Ri Kim, Kyoungmin Kim, and Jee-Hyong Lee. IJFIS 2017;17:329-36. https://doi.org/10.5391/IJFIS.2017.17.4.329