Performance Benchmarking of DETC, FG-TS, and MNL-UCB in Contextual Recommendation Tasks

Contextual multi-armed bandit (CMAB) algorithms have become a cornerstone of modern recommendation systems owing to their ability to effectively manage the exploration-exploitation trade-off in dynamic and uncertain environments. In this study, we conducted a comprehensive empirical comparison of three advanced CMAB algorithms: Double Explore-Then-Commit (DETC), Feel-Good Thompson Sampling (FG-TS), and Multinomial Logit Upper Confidence Bound (MNL-UCB). Leveraging the MovieLens 1M dataset, we constructed a realistic experimental setting by encoding detailed user profile features, including demographic and behavioral attributes, and generating low-dimensional movie embeddings using truncated singular value decomposition (SVD). We employed a logistic regression framework that captures the probabilistic nature of user preferences. The algorithms behaved markedly differently under long-term (10,000 rounds) and short-term (200 rounds) recommendation scenarios. It indicates that MNL-UCB achieves the lowest cumulative regret and shows strong performance stability across varying contexts in the long-term experiment. And FG-TS demonstrates robust adaptability in highly dynamic environments, making it particularly effective in scenarios with unpredictable behavior. However, DETC tends to underperform in complex contextual settings because of its lack of adaptability, leading to increased regret and fluctuating performance.