## 6 Conclusions  
For reinforcement learning recommendation for small and medium-sized data scenarios, considering dynamic interactive feedback, this paper presents a novel SMAB algorithm called DG-MOSS that utilizes adaptive episode in training, which is based on improved MOSS by incorporating dynamic grouping from environment feedback information to ensure the balance of exploration and exploitation and maximizing rewards for recommendations. This algorithm can increase the degree of exploration and exploitation according to the dynamic grouping, solving the insufficiency problem and making full use of feedback information. On the one hand, through analysis and proof, this paper gives the upper bound of the regret value of DG-MOSS, which provides strong theoretical support. On the other hand, extensive experiments were undertaken on four datasets (Random data-ND, Alibaba, Movielens, and Ads-CTR-Optimisation) with total rewards and average rewards. The maximum reward improvement compared to the second-best model reaches 1.02%, while the improvement compared to the worst baseline model can reach up to 36.32%. In summary, DG-MOSS is a stable and robust SMAB algorithm in recommendation. Extensive studies focus on combining deep learning and reinforcement learning to address problems. In the future, we will continue to investigate adaptive approaches based on big data by adding deep learning. Methodology, [PERSON]; supervision, [PERSON]; software, [PERSON]; writing--original draft, [PERSON]; formal analysis, [PERSON]; writing--review and editing, [PERSON] and X.Z. ; resources, [PERSON] All authors have read and agreed to the published version of the manuscript. This research is supported in part by the Program for Science and Technology Innovation Talents in the University of Henan Province under Grant No. 22 HASTIT014, the Science and Technology Research and Development Plan Joint Fund Project in Henan Province under Grant No. 222103810031, the International Science and Technology Cooperation Project in Henan Province \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithms** & **Original** & **1\% R** & **3\% R** & **5\% R** & **10\% R** & **1\% A** & **3\% A** & **5\% A** & **10\% A** \\ \hline \(\varepsilon\)-greedy & 0.0036 & 0.0075 & 0.0089 & 0.0076 & 0.0096 & 0.0041 & 0.0037 & 0.035 & 0.0040 \\ Boltzmann & 0.0133 & 0.0158 & 0.0153 & 0.0155 & 0.0199 & 0.0144 & 0.0151 & 0.0154 & 0.0155 \\ CNAME & 0.015 & 0.0156 & 0.0223 & 0.0178 & 0.0176 & 0.0284 & 0.0235 & 0.0252 & 0.0206 \\ UCB & 0.0136 & 0.0152 & 0.0195 & 0.0178 & 0.0182 & 0.0154 & 0.0189 & 0.0188 & 0.0196 \\ UCB1 & 0.0123 & 0.0148 & 0.0163 & 0.0157 & 0.0173 & 0.0155 & 0.0149 & 0.0158 & 0.0148 \\ FP-UCB & 0.0188 & 0.0202 & 0.0235 & 0.0233 & 0.0246 & 0.0214 & 0.0287 & 0.0265 & 0.0265 \\ BBANK & 0.0220 & 0.0241 & 0.0283 & 0.0266 & 0.0265 & 0.0291 & 0.0333 & 0.0343 & 0.0315 \\ MOSS & 0.0038 & 0.0071 & 0.0090 & 0.0078 & 0.0096 & 0.0038 & 0.0038 & 0.0038 & 0.0038 \\ R-MOSS & 0.0172 & 0.0145 & 0.0229 & 0.0220 & 0.0243 & 0.0228 & 0.0275 & 0.0239 & 0.0286 \\
**DG-** & **0.0223** & **0.0250** & **0.0283** & **0.0268** & **0.0280** & **0.0295** & **0.0339** & **0.0344** & **0.0316** \\
**MOSS** & **0.0224** & **0.0250** & **0.0283** & **0.0268** & **0.0280** & **0.0295** & **0.0339** & **0.0344** & **0.0316** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Random attacks (R) and average attacks (A) on the shopping dataset.