基于改进的深度Q网络结构的商品推荐模型

Commodity recommendation model based on improved deep Q network structure

传统推荐方法存在数据稀疏和特征识别差等问题,为了解决这些问题,根据隐式反馈构建具有时序性的正负反馈数据集。由于正负反馈数据集和商品购买具有强时序性特征,引入长短期记忆(LSTM)网络作为模型构件。考虑用户自身特征和用户动作选择回报由不同的输入数据决定,对竞争架构的深度Q网络进行改进,融合用户正负反馈和商品购买时序性,设计了基于改进的深度Q网络结构的商品推荐模型。模型对正负反馈数据进行区分性训练,对商品购买的时序性特征进行提取。在Retailrocket数据集上,与因子分解机(FM)模型、W&D模型和协同过滤(CF)模型中表现最好的相比,所提模型的准确率、召回率、平均准确率(MAP)和归一化折损累计增益(NDCG)分别提高了158.42%、89.81%、95.00%和67.57%。同时,使用DBGD作为探索方法,改善了推荐商品多样性低的缺陷。

Traditional recommendation methods have problems such as data sparsity and poor feature recognition.To solve these problems,positive and negative feedback datasets with time-series property were constructed according to implicit feedback.Since positive and negative feedback datasets and commodity purchases have strong time-series feature,Long Short-Term Memory(LSTM)network was introduced as the component of the model.Considering that the user’s own characteristics and action selection returns are determined by different input data,the deep Q network based on competitive architecture was improved:integrating the user positive and negative feedback and the time-series features of commodity purchases,a commodity recommendation model based on the improved deep Q network structure was designed.In the model,the positive and negative feedback data were trained differently,and the time-series features of the commodity purchases were extracted.On the Retailrocket dataset,compared with the best performance among the Factorization Machine(FM)model,W&D(Wide&Deep learning)and Collaborative Filtering(CF)models,the proposed model has the precision,recall,Mean Average Precision(MAP)and Normalized Discounted Cumulative Gain(NDCG)increased by 158.42%,89.81%,95.00%and 65.67%.At the same time,DBGD(Dueling Bandit Gradient Descent)was used as the exploration method,so as to improve the low diversity problem of recommended commodities.