Abstract:

The session-based recommendation algorithm based on Recurrent Neural Network （RNN） can effectively model the long-term dependency in the session， and can combine the attention mechanism to describe the main purpose of the user in the session. However， it cannot bypass the items that are not related to the user’s main purpose in the process of session modeling， and is susceptible to their influence to reduce the recommendation accuracy. In order to solve problems， a new paired coding scheme was designed， which transformed the original input sequence embedding vector into a three-dimensional tensor representation， so that non-adjacent behaviors were also able to be linked. The tensor was processed by a two-dimensional Conventional Neural Network （CNN） to capture the relationship between non-adjacent items， and a Neural Attentive Recommendation Machine introducing two-dimensional COnvolutional neural network for Session-based recommendation （COS-NARM） model was proposed. The proposed model was able to effectively skip items that were not related to the user’s main purpose in the sequence. Experimental results show that the recall and Mean Reciprocal Rank （MRR） of the COS-NARM model on multiple real datasets such as DIGINETICA are improved to varying degrees， and they are better than those of all baseline models such as NARM and GRU-4Rec+. On the basis of the above research， Euclidean distance was introduced into the COS-NARM model， and the OCOS-NARM model was proposed. Euclidean distance was used to directly calculate the similarity between interests at different times to reduce the parameters of model and reduce the complexity of model. Experimental results show that the introduction of Euclidean distance further improves the recommendation effect of the OCOS-NARM model on multiple real datasets such as DIGINETICA， and makes the training time of the OCOS-NARM model shortened by 14.84% compared with that of the COS-NARM model， effectively improving the training speed of model.

Key words: session-based recommendation, Recurrent Neural Network (RNN), paired coding, Conventional Neural Network (CNN), Euclidean distance

摘要：

虽然基于循环神经网络（RNN）的会话推荐算法可以有效地对会话中的长期依赖关系进行建模，并且可以结合注意力机制来刻画用户在会话中的主要目的，但它在进行会话建模的过程中无法绕过与用户主要目的不相关的物品，易受其影响以致降低推荐精度。针对上述问题，设计了成对编码方案来将原始输入序列嵌入向量转化为一个三维张量表示，使得非相邻的行为也能够产生联系。通过二维卷积神经网络（CNN）来处理该张量以捕获非相邻物品间的联系，并提出了引入用于会话推荐的二维卷积神经网络的神经注意力推荐机（COS-NARM）模型。该模型能有效跳过序列中与用户主要目的不相关的物品。实验结果表明，COS-NARM模型在DIGINETICA等多个真实数据集上的召回率和平均倒数排名（MRR）都得到了不同程度的提升，且优于NARM、GRU-4Rec+等所有基线模型。在上述研究的基础上，将欧氏距离引入COS-NARM模型，提出了OCOS-NARM模型。利用欧氏距离直接计算不同时刻兴趣间的相似度以减少模型的参数，降低模型的复杂度。实验结果表明，欧氏距离的引入不仅使得OCOS-NARM模型在DIGINETICA等多个真实数据集上的推荐效果得到了进一步的提升，而且使OCOS-NARM模型的训练时间相较COS-NARM模型缩短了14.84%，有效提高了模型的训练速度。

关键词: 会话推荐, 循环神经网络, 成对编码, 卷积神经网络, 欧氏距离