Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering

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.

Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk sampling

Word embedding has become ubiquitous and is widely used in various text mining and natural language processing (NLP) tasks, such as information retrieval, semantic analysis, and machine translation, among many others. Unfortunately, it is prohibitively expensive to train the word embedding in a relatively large corpus. We propose a graph-based word embedding algorithm, called Word-Graph2vec, which converts the large corpus into a word co-occurrence graph, then takes the word sequence samples from this graph by randomly traveling and trains the word embedding on this sampling corpus in the end. We posit that because of the stable vocabulary, relative idioms, and fixed expressions in English, the size and density of the word co-occurrence graph change slightly with the increase in the training corpus. So that Word-Graph2vec has 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 datasets show that the proposed algorithm outperforms traditional Skip-Gram by four-five times in terms of efficiency, while the error generated by the random walk sampling is small.