User Engagement:
Understanding and enhancing engagement is crucial, as it is heavily influenced by game outcomes.
Personalization Challenges:
Volatile, outcome-driven behaviors create unique challenges for real-time recommendations.
Optimized Frameworks:
EFfECT-RL, t-RELOAD, and ComParE use advanced methods to deliver tailored, effective recommendations.
Improved Metrics:
These frameworks show significant boosts in engagement and prediction accuracy on skill gaming platforms.
References
-
Debanjan Sadhukhan, Deepanshi Seth, Sanjay Agrawal, and Tridib Mukherjee.
"EFfECT-RL: Enabling Framework for Establishing Causality and Triggering
engagement through RL." In Proceedings of the 33rd ACM International
Conference on Information and Knowledge Management, pp. 4836-4843. 2024.
Link:
https://dl.acm.org/doi/abs/10.1145/3627673.3680058
-
Debanjan Sadhukhan, Sachin Kumar, Swarit Sankule, and Tridib Mukherjee.
"t-RELOAD: A REinforcement Learning based Recommendation for Outcome-driven
Application." In Proceedings of the Third International Conference on AIML
Systems, pp. 1-7. 2023. Link:
https://dl.acm.org/doi/abs/10.1145/3639856.3639884
-
Mukherjee, Koyel, Deepanshi Seth, Prachi Mittal, NuthiS. Gowtham, Tridib
Mukherjee, Dattatreya Biswas, and Sanjay Agrawal. "ComParE: A User Behavior
Centric Frameworkfor Personalized Recommendations in Skill GamingPlatforms."
In Proceedings of the 5th Joint International Conference on Data Science
& Management of Data (9th ACM IKDD CODS and 27th COMAD), pp. 186-194.
2022. Link:
https://dl.acm.org/doi/abs/10.1145/3493700.3493733
-
Eswaran, Sharanya, Mridul Sachdeva, Vikram Vimal, Deepanshi Seth, Suhaas
Kalpam, Sanjay Agarwal, Tridib Mukherjee, and Samrat Dattagupta. "Game
action modeling for fine grained analyses of player behavior in multi-player
card games (rummy as case study)." In Proceedings of the 26th ACM SIGKDD
International Conference on Knowledge Discovery & Data Mining, pp.
2657-2665. 2020. Link:
https://dl.acm.org/doi/abs/10.1145/3394486.3403316
