一种基于KCNN和MKR的两阶段深度学习多任务推荐模型

A two-stage deep learning multi-task recommendation model based on KCNN and MKR

深度学习和知识图谱的结合在推荐领域受到广泛关注,然而部分模型输入向量较为稀疏,不仅增加了整个模型的训练难度,还容易导致模型陷入局部最优;此外,大多推荐模型并未充分挖掘用户和物品的特征交互,使得用户和物品的向量表示不准确,影响最终的推荐模型性能。基于此,本文提出一种基于KCNN和MKR的两阶段深度学习多任务推荐模型TMR。首先,利用文本卷积网络,提取物品名称的特征,将其转化为稠密向量,再结合物品自身属性,作为物品特征向量的初始化表示;其次,采用交替训练的方式,获取知识图谱中的辅助信息,再以DeepFM为特征提取层,挖掘用户(user)和物品(item)的特征交互。实验结果表明:与当前主流推荐方法相比,TMR模型在准确度等评价指标上有很好的表现,提高了推荐系统的性能。

The combination of deep learning and knowledge graphs has attracted the attention of researchers in the field of recommender system.However,some models use sparse feature vectors as input which will not only increase the training difficulty of the model,but also cause the model to fall into a local optimum.In addition,most models can′t fully describe the relationship between the items in the recommender system and the entities in the knowledge graphs.The vector representation is not accurate and it will affect the performance of the recommender system.Based on these issues,a multi-task recommendation model based on two-stage deep learning(TMR)is proposed.First,using KCNN to extract the features of the items′name,convert it into a dense vector,and then combine the property of the item as the initial representation of the item feature vector.Second,alternate training is used to obtain extra information in the knowledge graph,and then DeepFM is used as a feature extraction layer to mine the feature interaction between user and item.The experiments demonstrate that our proposed TMR model achieves better results in several evaluation indicators and improves the performance of the recommender system,comparing with other state-of-the-art methods.