Item2vec与改进DDPG相融合的推荐算法

Recommendation Algorithm Combining Item2vec with Improved DDPG

传统的推荐算法能够有效解决信息过载问题,但在冷启动和数据稀疏的情况下,传统方法仍有其局限性。针对以上问题本文提出一种基于深度强化学习理论的推荐算法,该算法使用深度确定性策略梯度(DDPG,deep deterministic policy gradient,DDPG)算法来解决推荐问题,使用Item2vec将离散的动作空间转换为连续的表示,同时提出了一种余弦距离和欧氏距离相结合的奖励函数,能够保障神经网络不会过早的收敛于局部最优。应用该算法进行电影的推荐,实验结果表明本文提出的算法能够产生较好的推荐并能缓解冷启动所带来的影响。

The traditional recommendation algorithm can effectively solve the problem of information overload, but in the case of cold start and data sparse, the traditional method still has its limitations. For these problems, this paper proposed a recommendation algorithm based on deep reinforcement learning theory, which using the deep deterministic policy gradient (DDPG) algorithm to solve the recommendation problem and using Item2vec to transform the discrete action space into a continuous representation. A reward function combining cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely. This paper applied the algorithm to movie recommender system. The final experiment proved that the proposed algorithm can generate better recommendation results and alleviate the impact of cold start.