  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.
