Current item-item collaborative filtering algorithms based on artificial neural network, such as Item2vec, have become ubiquitous and are widely applied in the modern recommender system. However, these approaches do not apply to the large-scale item-based recommendation system because of their extremely long training time. To overcome the shortcoming that current algorithms have high training time costs and poor stability when dealing with large-scale data sets, the item graph embedding algorithm Item-Graph2vec is described here. This algorithm transforms the users' shopping list into a item co-occurrence graph, obtains item sequences through randomly travelling on this co-occurrence graph and finally trains item vectors through sequence samples. We posit that because of the stable size of item, the size and density of the item co-occurrence graph change slightly with the increase in the training corpus. Therefore, Item-Graph2vec has a stable runtime on the large scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world data sets demonstrate that Item-Graph2vec outperforms Item2vec by 3 times in terms of efficiency on douban data set, while the error generated by the random walk sampling is small.

翻译：摘要：当前基于人工神经网络的物品-物品协同过滤算法（如Item2vec）已广泛应用于现代推荐系统中。然而，由于训练时间过长，这些方法不适用于大规模基于物品的推荐系统。为克服现有算法在处理大规模数据集时训练时间成本高、稳定性差的缺陷，本文提出物品图嵌入算法Item-Graph2vec。该算法将用户购物列表转换为物品共现图，通过在该共现图上随机游走获得物品序列，最终通过序列样本训练物品向量。我们假设，由于物品规模具有稳定性，物品共现图的规模与密度会随训练语料的增加而产生微小变化。因此，Item-Graph2vec在大规模数据集上具有稳定的运行时间，且其性能优势随训练语料增长而愈发显著。在真实数据集上进行的广泛实验表明，在豆瓣数据集上，Item-Graph2vec在效率上比Item2vec提升3倍，同时随机游走采样产生的误差较小。